Advertised Project Openings


Below are currently advertised UROP projects available to eligible undergraduates. All projects, regardless of mode (pay, credit, or volunteer) are expected to be worth MIT academic credit and be supervised by MIT faculty. Projects appear on this list in the order they have been received.

These projects do not represent all available UROPs as many faculty do not submit project listings for this site. Rather, they expect interested students to contact them based on their general research to discuss potential UROPs.

Available UROPs

1/18/19

Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Cynthia Breazeal

Project Title: AI Software Development for the Bridge K-12 Education Initiative

Project Description: Are you interested in democratizing AI education? Help us build an interactive platform for students of all ages to learn about AI!

From Scratch-based microworlds to Jupyter notebooks, we’re developing the software services and infrastructure that will give K-12+ students a hands-on, project-based introduction to fundamental concepts and practices of AI: machine perception, machine learning, knowledge representation, human-AI interaction, autonomous systems, ethical design issues, and more.

Potential responsibilities include designing, implementing, and testing the software infrastructure for a variety of educational AI tasks and projects; developing high quality code; establishing goals and remaining on schedule. There will be iterative development via field-testing with students. You will be working with graduate students and faculty who are helping to develop the curriculum and projects. If you have a background in AI and machine learning, you can contribute to these topics, too!

Required: Programming experience in JavaScript and Python

Not required but will be significantly helpful: Experience with machine learning software development and engineering practices; experience with TensorFlow and/or PyTorch; experience with git-based version control and continuous integration; experience integrating with large cloud service providers (e.g. GCP, IBM Cloud, AWS); experience with Scratch 3.0 or Snap!, front-end UX design and development.

Prerequisites: Programming experience in JavaScript and Python

Contact: Katherine Gallagher: kvg0@mit.edu


1/18/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Canan Dagdeviren

Project Title: Interfacing Miniaturized Electromechanical Sensor for Monitoring Tissue Moduli

Project Description: We are developing a micro sensor for monitoring tissue stiffness with wearable/implantable applications. This UROP position is focused on developing the interfacing electronics, and potentially taking part in fabricating the prototypes in a cleanroom environment. The student will be working closely with the mentor in the followings:

  1. Building interfacing circuit/characterization equipment set-up (writing interfacing programs etc.)
  2. Building prototype devices for proof-of-concept experiment
  3. Literature review and documentation of experimental results

Prerequisites:

  1. Experience in analog/digital circuit development
  2. Experience in signal integrity (simulation or hands-on) and low level signal conditioning is a plus
  3. Responsiveness and dedication

Relevant URL: https://www.nature.com/articles/nmat4289

Contact: Zijun Wei: zijunw@mit.edu


1/18/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Canan Dagdeviren

Project Title: Simulation and characterisation for flexible piezoelectrical micro-devices

Project Description: In daily life, human body intrinsically and continuously generates diverse physiological signals, such as electrical, mechanical, biochemical, acoustic, and magnetic signals to reflect the health status. Our lab, Conformable Decoders, works on translating these biological signals around us - especially from the human body - into energy, imaging, and data for early detection of diseases. We microfabricate the devices for energy harvesting and sensing in our very own cleanroom (YellowBox) at the Media Lab. In this project, the student will collaborate closely with the student advisor to (1) characterise the micro structure of a flexible piezo-electric sensor and actuator and energy harvester, (2) simulate and calculate the energy density of the flexible energy harvester, (3) simulate the performance of the flexible ultrasonic actuator in soft tissue. In the meanwhile, on the basis of a well-established model, the student will help the student advisor (4) optimize a structure of a flexible piezo-electric energy harvester and ultrasonic actuator.

In this project, the UROP will be taught how to operate the testing equipment such as oscilloscope, National Instrument system, impedance analyser, laser doppler vibrometer, etc, process recorded data, and write academic report. In addition, you will facilitate the student advisor to set up experiments and review literatures.

Prerequisites: We are looking for one UROP who is self-motivated, methodical, careful and organized and diligent. First-hand experience on Comsol Multiphysics for simulation is a plus.

Contact: Tao Sun: taosun01@media.mit.edu


1/18/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Mitch Resnick

Project Title: Prototyping Learning Experiences with Public Libraries

Project Description: The Public Library Innovation Exchange (PLIX) team (Part of the ML Learning Initiative) is looking for a UROP to work with us to prototype and test activities with public libraries.

This will include working with existing projects (e.g., personal food computer, LEGO Wayfinder, micro:bit, cube satellites) and building activities and experiences that libraries will be able to use. We will be exploring the different levels of learning that happen in a library, and building different types of experiences (with the 4 P’s of learning in mind: https://learn.media.mit.edu/lcl/).

UROPs will also be documenting their activities on the PLIX website, as well as working with public librarians and patrons to co-design, test activities, and receive feedback.

We are looking for someone who:

  • Enjoys facilitating activities with people of various ages.
  • Is excited to design activities for informal learning spaces.
  • Is familiar with user-centered design.
  • Is fond of public libraries.

Relevant URL: https://plix.media.mit.edu/activities/

Contact: Maggie Cohen: cohenm@media.mit.edu


1/18/19

Spring

Multiple Openings

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Tomaso Poggio

Project Title: Deep Learning: Biological Plausibility, Computer Vision and Infrastructure

Project 1: Backpropagation (BP) has been long criticized to be somewhat not biologically-plausible. However, there are variants of BP that are more implementable by the brain and at the same time perform comparably to BP. In recent experiments, we have promising results scaling them to more real-world tasks.

Students are encouraged to either:

  1. Try new variants of this class of algorithms on new tasks.
  2. Propose and try other biologically-plausible learning algorithms for training neural networks.

Project 2: Although humans intuitively understand/interpret the world in terms of discrete objects. State-of-the-art machine vision systems do not have a good representation of visual objects. In a series of recent work, we try to incorporate the knowledge of object into deep learning networks and proposed a class of models we call "object-oriented" deep networks. https://cbmm.mit.edu/publications/object-oriented-deep-learning, https://dspace.mit.edu/handle/1721.1/113002

Students are encouraged to either:

  1. Implement "object-oriented" deep networks with PyTorch Tensorflow and test its performance on wider range of computer vision tasks
  2. Try new variants of this class of models.
  3. (Advanced) Accelerate "object-oriented" deep networks with customized NVIDIA CUDA code

Project 3: Deep Learning Framework/Infrastructure. We are developing a new deep learning framework that offers a greater level of flexibility and modularity for research. Students who are interested in coding/understanding neural networks from scratch can benefit from helping developing a deep learning framework.  We also need a web developer for developing the website for this framework.

Prerequisites: Python, Matlab, C++ or C.

Relevant URL: https://cbmm.mit.edu/about/job-opportunities/mit-urop-masters-students-undergrads-join-cbmm-engineering-intelligence-team

Contact: Qianli Liao: LQL@mit.edu

 


1/18/19

Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Ernest Fraenkel

Project Title: Biologically Interpretable Autoencoders and Neural Networks

Project Description: The project focuses on the implementation and development of biologically interpretable neural networks and autoencoders.

Prerequisites: No prior research experience is necessary. Strong familiarity with Python and some experience with TensorFlow/Keras for neural networks is required.

Contact: Maxwell Gold: mpgold@mit.edu


1/18/19

Spring

UROP Department, Lab or Center: Sloan School of Management (Course 15)

MIT Faculty Supervisor Name: David R. Keith

Project Title: Machine learning models of household electric vehicle adoption

Project Description: Understanding what motivates consumers to adoption electric vehicles is critical if the transition to sustainable mobility is to be achieved. Analyzing an unusually large and detailed dataset, containing household-level vehicle registration data for millions of households in the United States, this project will use machine learning to develop predictive models of household electric vehicle adoption.

Prerequisites: Prior experience in the development of machine learning models.

Contact: David Keith: dkeith@mit.edu


1/18/19

IAP

UROP Department, Lab or Center: Comparative Media Studies (21 CMS)

MIT Faculty Supervisor Name: Vivek Bald

Project Title: Multi-tiered Log-on/Permissions System for Interactive Website (Lost Histories Project)

Project Description: Faculty member in Comparative Media Studies and the Open Documentary Lab is looking for a student to help conceive and build out a multi-tiered log-on/credentials system for a web-based community history project.

The Lost Histories Project is focused on recovering the stories of Muslim steamship workers from present-day Bangladesh who jumped ship in the U.S. between 1910-1950, and settled and married within African American and Puerto Rican communities in New York, Detroit, and elsewhere. (More info: bengaliharlem.com). We seek to create a digital space where the descendents of this community, now in their 60s and 70s, can upload audio, video, images, and text to tell their families’ stories, and to interact with one another and the larger public.

The broad research question that animates this work is: how can we use the visual, informational, and interactive affordances specific to the web to create spaces where people from underrepresented communities can tell their own stories, and through this process, build their own crowd-sourced, collectively-produced multi-vocal histories.

The UROP student will work with the project director to conceive and build out a structure of roles and permissions that will foster the participation of groups of people within a family: i.e.: one family member who will have an admin role in which they can upload new material (audio, video text) to create new "stories" on the site; other family members who might have a participant role in which they have permission to add content (stories, annotations, commentary) to existing stories; and others who are members of other families within the same community who may have permissions to participate in more delimited ways. In other words, we need to figure out a log-on system that differentiates between different levels of participants in this project – admins, contributors, commenters, etc. – and assigns different sets of permissions with regard to submitting, editing, and approving uploaded content.

An initial UI/UX design and db structure for this project have been completed, with a functional web-based front-end already built out, but we are still iterating and adding functionality. Current build uses React to power the front end and webpack as a build environment. Backend server is node.js; MongoDB is used for storing metadata, Amazon S3 is used for storing media, and we are currently relying on Heroku for hosting both experimental/staging and stable versions of the web app as it is built out.

We are looking for someone, ideally, who has prior skills and experience across the stack described above.

______________

Project #2: Web-based Application to Tag Photos with Audio Recordings (Lost Histories Project)

Project Description: Faculty member in Comparative Media Studies and the Open Documentary Lab is looking for a student to help conceive, build, and iterate an application for a web-based community history project that will allow users to tag photos with audio recordings.

The Lost Histories Project is focused on recovering the stories of Muslim steamship workers from present-day Bangladesh who jumped ship in the U.S. between 1910-1950, and settled and married within African American and Puerto Rican communities in New York, Detroit, and elsewhere. (More info: bengaliharlem.com). We seek to create a digital space where the descendents of this community, now in their 60s and 70s, can upload audio, video, images, and text to tell their families’ stories, and to interact with one another and the larger public.

The broad research question that animates this work is: how can we use the visual, informational, and interactive affordances specific to the web to create spaces where people from underrepresented communities can tell their own stories, and through this process, build their own crowd-sourced, collectively-produced multi-vocal histories.

The UROP student will work with the project director to conceive and build out a web application that will allow users who have logged on with appropriate credentials to click specific photographs on the site and annotate them: either by entering text or by accessing the microphone on their computer, phone, or tablet to record and upload commentary/stories related to the photo.

An initial UI/UX design and db structure for this project have been completed, with a functional web-based front-end already built out, but we are still iterating and adding functionality. Current build uses React to power the front end and webpack as a build environment. Backend server is node.js; MongoDB is used for storing metadata, Amazon S3 is used for storing media, and we are currently relying on Heroku for hosting both experimental/staging and stable versions of the web app as it is built out.

We are looking for someone, ideally, who has prior skills and experience across the stack described above.

Contact: Vivek Bald: vbald@mit.edu


1/17/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Danielle Wood

Project Title: Designing Systems to Combat Invasive Plant Species in West Africa

Project Description: The Space Enabled Research Group advances justice in Earth’s complex systems using designs enabled by space. Space technology contributes to the United Nations’ Sustainable Development Goals via communication, earth observation, positioning, microgravity research, spinoffs and basic research. Space Enabled uses six methods: art, design, social science, complex systems, satellite engineering and data science. This project applies all six methods in collaboration with a company based in Benin called Green Keeper Africa that harvests the invasive water hyacinth and uses it to manufacture products that clean oil-based waste. Green Keeper Africa faces a challenge to monitor the location of the water hyacinth; they propose to create an Observing System to track the plant. Space Enabled and Green Keeper Africa are collaborating on a multi-faceted research project that will harness all six of the Space Enabled research methods. The project uses design thinking to identify the objectives for an information system. The social science portion examines the historical, economic and cultural context. The complex system modeling activity builds a computer-based simulation of the community and environment. The engineering component explores approaches to produce new data about the water hyacinth. The data science work builds a prototype system with actionable information about the water hyacinth. Students will join the activities outlined above, depending on their interests and background. The option to renew for summer may be available.

Prerequisites: We are looking for students with a technical background in biology; urban studies; social science (history, anthropology, sociology, economics); civil, environmental, mechanical and/or aerospace engineering; or computer science/data science. In addition to this technical background, we seek students with an interest in sustainable development. Prior experience with satellite remote sensing data analysis or Geographic Information Systems is preferred.

Relevant URL: spaceenabled.media.mit.edu

Contact: Javier Stober: stober@mit.edu


1/17/19

Spring/Summer

UROP Department, Lab or Center: Brain and Cognitive Sciences (Course 9)

MIT Faculty Supervisor Name: Professor John Gabrieli

Project Title: How do the best among us remember?

Project Description: This project is aimed at understanding how some people remember better than others. We will use a combination of brain imaging and experimental interviews to understand what differentiates individuals in their short-term memory abilities. Research assistants will have the opportunity to help shape and implement the project. Opportunities for presentation and publication on this work will also be made available. Please contact soon.

Prerequisites: An interest in psychological and brain science research.

Contact: Dr. Nicholas Hubbard: nhubbard@mit.edu


1/17/19

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Joseph A. Paradiso

Project Title: Aerospace-Grade Electronic Textiles

Project Description: The outermost skin of a space-based structure is designed using materials known to protect against the harsh elements of space. Simultaneously, the skin provides a unique opportunity to characterize the environment proximate to a spacecraft and to perform real-time damage detection. Thus, we are developing an aerospace-grade fabric that simultaneously senses and protects, emulating the dual protective and sensory capabilities of biological skin. Aerospace-grade sensory skins will serve a key role in next generation haptic feedback systems for spacesuits, as well as next generation thermal blankets for distributed detection of high velocity debris impact.

Prerequisites: I am interested in working with 1-2 advanced undergraduates (juniors/seniors, or a very dedicated sophomore). Various sub-projects exist that we can define based on your skills and interests. Primarily looking for people with background in aerospace engineering, materials science, electrical engineering, and/or physics. If you work well with someone, you are welcome to apply as a pair. If you have interest in weaving/knitting or textile design as an art form, I'd also be interested in discussing opportunities with you.

Relevant URL: https://www.media.mit.edu/projects/SpaceSkin/overview/

Contact: Juliana Cherston: cherston@mit.edu


1/17/19

Spring/Summer

Multiple Openings

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Pattie Maes

Project #1: AI that asks Curious Question about You

Project Description: Using AI to ask curious questions to quickly annotate thousands of images and videos Annotating data is a time-consuming and often technically difficult process. However, to create personal AIs, one needs to annotate their own data. Our idea is to create a voice assistant that comes up with the minimum number of maximally informative questions to annotate a large dataset. Read this story for more details: https://goo.gl/b9STRt

What you will learn:

  1. Using state of the art Deep Learning for extracting knowledge from raw data
  2. Understanding Deep Reinforcement Learning and Artificial Curiosity
  3. Understanding Graph Embedding and Link Prediction using Deep Learning
  4. Building a curious chatbot!

Prerequisites:

Theory:

  1. Linear Algebra
  2. Natural Language Processing
  3. Machine Learning

Practice:

  1. Python + Numpy + Matplotlib
  2. PyTorch or TensorFlow

Other:

  1. Diligence and dedication to work with a team of MIT and Harvard graduate students.
  2. Passion for inventing new technologies and change the world.

Relevant URL: https://goo.gl/b9STRt

_____________

Project #2: Augmenting Human Memory through AI and HCI

Project Description: Use Artificial Intelligence to create new interfaces We are building a new interface that uses thousands of personal images, videos and other forms of data to create a personal knowledge graph. The graph is then fed to an end-to-end neural question answering system that is capable of answering personal questions using the data. We hope that such a system can operate as a prosthetic episodic memory system for patients with memory problems.

What you will learn:

  1. Building iOS apps that use modern machine learning
  2. Using state of the art Deep Learning for extracting knowledge from raw data
  3. Understanding and building futuristic Data-driven User Interfaces
  4. Presenting and writing original ideas for publication in world-class venues.

Prerequisites:

Theory:

  1. Linear Algebra
  2. Image Processing
  3. Machine Learning

Practice:

  1. iOS app development
  2. Python + Numpy + Matplotlib
  3. PyTorch or TensorFlow

Other:

  1. Diligence and dedication to work with a team of MIT and Harvard graduate students.
  2. Passion for inventing new technologies and change the world.

_____________

Project #3: Deep Neurofeedback

Project Description: Video Generation from Brain Waves In this project, we are using the power of modern Deep Learning for extracting data from the human brain and turning it into a stream of video. See this recent paper as a prior work done in this direction: http://crcv.ucf.edu/papers/camera_ready_acmmm_BNI08.pdf 

Our system consists of an EEG headset, a recurrent encoder network to encode the EEG signals into feedback latent-space and a decoder/generator network that maps the latent space signals into a sequence of images and audio.

What you will learn:

  1. Recording and analyzing brain activity using the state of the art EEG sensors
  2. Implementing modern deep neural networks such as Generative Adversarial Networks, Variational Autoencoders, Seq2Seq models etc.
  3. Presenting and writing original ideas for publication in world-class venues.

Prerequisites:

Theory:

  1. Linear Algebra
  2. Image Processing
  3. Machine Learning

Practice:

  1. Python + Numpy + Matplotlib
  2. PyTorch or TensorFlow

Other:

  1. Diligence and dedication to work with a team of MIT and Harvard graduate students.
  2. Passion for inventing new technologies and change the world.

Relevant URL: https://docs.google.com/document/d/1QuVcSV0nmfENOdIFavsAFzt-4goOvSxbRYBC7K0ZLtM/edit?usp=sharing

Contact: Neo (Mostafa) Mohsenvand: mmv@mit.edu


1/17/19

Spring/Summer

UROP Department, Lab or Center: Sloan School of Management (Course 15)

MIT Faculty Supervisor Name: Robert Pindyck

Project Title: Climate Change Policy -- Abatement vs. Adaptation

Project Description: I am looking for one or two students to help with research related to climate change policy, and environmental policy more generally. My focus is on the relative costs and benefits of two policy instruments:  adaptation to climate change (and to other kinds of environmental damages), versus abatement of GHG emissions (or other pollutants) in order to avoid climate change (and other environmental damages).  Some of this work will involve an extensive literature review in support of a policy-oriented paper I am developing.  In addition, I am working on a theoretical model that explores how various forms of uncertainty can affect the adaptation-abatement trade-off; numerical solutions of the model are done using MATLAB.

The work will be done during the spring semester, and could continue into the summer.

Prerequisites: Candidates should also have a good background in economics, be able to work independently, and for the modeling work, have experience with MATLAB.  

Contact: If you are interested, please send a resume and transcript to: Professor Robert Pindyck, Sloan School of Management, Room E62-522, rpindyck@mit.edu.


1/17/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Canan Dagdeviren

Project Title: A miniaturized neural interfacing device for long term chronic implantation

Project Description: Our body is an ocean of patterns that we have not yet properly decoded. Our lab, Conformable Decoders, works on translating these biological signals around us - especially from the human body - into energy and data that we can easily understand. We microfabricate the devices for energy harvesting and sensing in our very own cleanroom (YellowBox) at the Media Lab. In this project, the student will collaborate closely with the student advisor to fabricate tools needed to test the devices and conduct tests in the cleanroom.

The device mentioned is an implantable multi-functional neural probe for electrical stimulation, recording, and drug delivery which will be used for the treatment and management of neurodegenerative disorders, chiefly Parkinson’s Disease.

Depending on your expertise and interest, tasks may include:

  1. Fabrication of mf-MiNDS components and electrical testing and validation, in vitro infusion characterization etc.
  2. Animal testing of the devices in rats
  3. Literature review and summarization of neural probe implantation, clinical biomarkers associated with Parkinson’s Disease, as well as combinatorial therapies for neurodegenerative disorders.

We are looking for one UROP

Prerequisites: We would like students who are careful, methodical, organized, and motivated. Working in the cleanroom as an undergraduate is an amazing opportunity - you will appreciate it. Though not required, prior experience in a research laboratory is preferred.  Exposure to fabrication (laser cutter, CNC/manual mill, simple electronics, etc.) and any experience in working with mice is a plus.

Contact: Nikita Obidin: nikitaob@media.mit.edu


1/17/19

IAP/Spring

UROP Department, Lab or Center: Urban Studies and Planning (Course 11)

MIT Faculty Supervisor Name: Mariana Aracaya

Project Title: Urban Metrics

Project Description: The overall aim of this project is to construct urban form metrics using a combination of remote sensing and geospatial techniques. In particular, this project examines the persistence of urban form through the study of the first housing initiative in the United States. This initiative planned more than one hundred new neighborhoods in twenty-four states. Surprisingly, we know very little about the location of these developments and how their built form has had a lasting impact on the social dynamics of their inhabitants.

This project is offering motivated students interested in computation and social sciences an opportunity to explore new approaches to measure the built environment. The goal of this study is to use CAD software to digitize neighborhood plans, to geocode them using GIS software, and to process satellite images and vectored plans to construct a series of built form metrics. The final deliverable will be a dataset containing the spatial metrics for each digitized neighborhood.

Prerequisites: The team is looking for someone that is excited about applying geospatial methods to study the built environment, and who is comfortable with statistics (though not required). More importantly, we welcome MIT students excited about testing different approaches, challenging ideas, and getting into the data.

Contact: Arianna Salazar Miranda: ariana@mit.edu


1/17/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Hiroshi Ishii

Project Title: Magnet-Sheet: Computationally Controlled Coil Embedded Flexible PCB to Render Objects and Texture

Project Description: Within the field of human computer interaction, this project explores the design space of using sheet interfaces and in particular flexible PCB. A sheet, a table cloth, a gaming mat, a foldable or rollable map, and a paper craft are the main scenarios. The flexible PCB generates a magnetic field to move objects, surfaces, or haptic pattern. The current stage of the project has achieved control over objects dynamically manipulated on the flexible PCB surface. In the Spring semester, we aim to build the electronics of two flexible PCB and design/fabricate. Please contact us for details

Prerequisites:

  • Has experience with circuits or design circuits, electromagnetic fields, andcoding with C (Arduino).
  • Mechanical Engineer, or EECS students

Contact: Nikolaos Vlavianos (nv2247@media.mit.edu), RA Tangible Media Group, PhD Student Design and Computation


1/11/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Canan Dagdeviren

Project Title: Intelligent textile suit for multi-modal spatiotemporal health sensing

Project Description: There is a paradigm shift in the way we provide healthcare, from curing to preventing to constant care, and from hospital-centered to patient-oriented health. We aspire medical technology that is not only accessible, accurate, and comfortable, but can also be functional anywhere and anytime to improve our health and wellbeing.

The Conformable Decoders group are currently developing a personalized intelligent garment through digital knitting technologies for large-scale physiological sensing and activity monitoring. We have come up with a technique that combines thin flexible-stretchable electronic devices including interconnect lines and commercial integrated circuits with elastomeric substrates that can be woven into knitted textiles using a commercial manufacturing process. Similar to a compression garment, the nature of this knitted textile will allow more intimate contact between electronics and the skin

Due to the multidisciplinary nature of this project, depending on the background, skills, and interests, the UROP student(s) will be expected to perform one/several of the followings:

  • Populate and connect sensor nodes with interconnects and integrate them into fabrics
  • Develop wireless communication interface and analyze sensor data
  • Design and model textile and clothing patterns

Prerequisites: Are independent, dedicated, imaginative, and creative. Possess great organizational and communication skills. Are interested and experienced in one or more of the followings: wearables, sensor interface, wireless communication, PCB design, textile and apparel design, illustration, and/or programming.

Contact: To take a part in this exciting project, please send your resume and interest about this project to Irmandy Wicaksono: irmandy@mit.edu


1/11/19

Spring/Summer

UROP Department, Lab or Center: Brain and Cognitive Sciences (Course 9)

MIT Faculty Supervisor Name: Mark Bear

Project Title: Characterizing visual cortical activity during recovery from amblyopia

Project Description: Amblyopia is the most common form of visual disability in children and arises from poor visual quality early in life.  Disparities in vision between the two eyes early in life can lead to a lasting deficit in how the cortex processes visual information, which can eventually lead to blindness in the weaker eye.  The overall goal of this project is to identify potential low-cost, noninvasive treatments for amblyopia using a mouse model. The role of the UROP student will be to characterize visual ability following treatments aimed at ameliorating visual deficits.  The student will learn to handle mice, conduct electrophysiological recordings in visual cortex, and perform histological analyses.

Prerequisites: The UROP student must be motivated, organized, reliable, and eager to learn about visual cortical plasticity.  Experience using MATLAB is a plus, though not required.  Students from any major are welcome to apply.

Contact: Daniel Montgomery: danielpm@mit.edu


1/11/19

Spring

UROP Department, Lab or Center: Brain and Cognitive Sciences (Course 9)

MIT Faculty Supervisor Name: J. Troy Littleton

Project Title: Investigating cell-type specific mechanisms of synaptic communication in the simple animal models of autism

Project Description: The Littleton lab seeks to understand the molecular mechanisms of synaptic signaling during normal brain function and in disease models using Drosophila as the model system. To address this, we use Drosophila genetics, molecular biology, calcium imaging, optogenetics, electrophysiology, super-resolution microscopy, single-cell RNAseq, biochemistry, and behavior assays.  We are looking for biology /BCS student/course 6 students to assist our efforts to carry out molecular biology, genetic screens, developing analytical tools to analyze imaging data and behavioral assays. We will provide support and training in genetics, molecular biology, microscopy, and behavioral assays. We offer a friendly environment for your training and learning. 

We are interested in addressing the following: 

  • (project 1) Investigating the molecular mechanisms of synaptic signaling in animal models of autism, 
  • (project 2) Tracking the crawling behavior of larval Drosophila (maggots) in disease models of autism, 
  • (project 3) Developing analytical tools to analyze synaptic calcium imaging data.

Prerequisites: Great curiosity and enthusiasm to pursue multidisciplinary research. For project 1, some background in molecular biology and/or genetics might be preferred. For project 2 and 3, some background in MATLAB or python is strongly preferred.

Relevant URL: https://littletonlab.mit.edu/home

Contact: Suresh Kumar Jetti: sureshj@mit.edu


1/10/19

IAP/Spring

UROP Department, Lab or Center: Environmental Solutions Initiative (ESI)

MIT Faculty Supervisor Name: John E. Fernandez

Project Title: ESI UROP Program - Climate, Cities, & Consumption

Project Description: The MIT Environmental Solutions Initiative (ESI) sponsors and manages research positions available to undergraduates interested in a wide variety of environmental topics. These research experiences are organized by the ESI in collaboration with all five schools at MIT and dozens of professors and many research labs across the Institute. Any undergraduate in any major, including undeclared, are eligible to apply for a UROP through the ESI. The mission of the ESI includes activities that support the substantial scientific, engineering, policy, and design capacity of MIT to create solutions to today’s environmental challenges. ESI UROPs are a key element of this mission.

Research opportunities are available in each of the following three domain areas:

  • Climate science and Earth systems
  • Cities and infrastructure
  • Sustainable production and consumption

Detailed descriptions of each of these areas is available on the ESI web site; https://environmentalsolutions.mit.edu/wp-content/uploads/2018/10/ESIAgenda_rev.-10-2018.pdf

Active ESI program research opportunities: Currently, the following programs are accepting interest from prospective UROP candidates. All of these topics are regularly updated and may change as projects are completed and new opportunities arise. You may specify one or more areas of interest in your initial contact with the ESI (see below for the Application process).

  1. Plastics and the Environment (Sustainable Production and Consumption) The ESI has launched a large-scale effort to contribute to the multi-faceted challenge of plastic in the environment. The project is comprised of work in three areas; 1) Material design for environmentally benign plastics; 2) Sensing microplastics in the environment and; 3) Modeling the primary sources and dispersion of microplastics. UROP supervisor: various professors and ESI staff
  2. Mining and the Environment (Sustainable Production and Consumption) The ESI has launched a program focused on reducing the environmental impact of mining across the world. The program is in the initial stage reviewing the scientific and engineering literature and articulating a research agenda for a major mining conglomerate. UROP supervisor: various professors and ESI staff
  3. Nature and Climate Change (Climate science and Earth systems) The ESI is working on topics in biodiversity, the reduction of deforestation, community engagement in protecting fragile ecosystems, and other elements of the relationship between nature and the climate. One of ESI’s primary partners, Conservation International, has hosted students as guests in field stations in the development of technologies that aid conservation efforts. Emerging projects within ESI’s portfolio include a project to address river pollution in Indonesia and a project to reduce deforestation in Colombia. UROP supervisor: Prof. Fernández and other MIT professors and researchers
  4. Cities and Climate Change (Cities and Infrastructure) The ESI offers opportunities through several cities including New York City, Beijing, Chengdu, Hong Kong, Ben Guerir (Morocco) and others in a variety of topics ranging from measures of resilience and sustainability to mitigating carbon emissions and adaptation to climate change. UROP supervisor: Prof. Fernández and other MIT professors and researchers
  5. Climate Change in the US (Climate science and Earth systems) The ESI engages communities across the US in understanding and considering the consequences of a changing climate. Goals of the work include increasing the salience of climate change for people in their daily lives across a wide range of socioeconomic, political, and cultural perspectives. UROP supervisor: Laur Hesse Fischer (ESI) and others

Application process: The ESI will directly host UROP candidates in any of the research opportunities listed above. The ESI will also assist students in connecting with professors and researchers across the Institute working in issues that align with ESI priorities and serve to advance work in key domain areas and programs.

Please take the following steps in applying to an ESI UROP:

  1. Send an email to Prof. John E. Fernández, fernande@mit.edu, stating your general interest and your preferred program research opportunity. Please list more than one and as many as three preferences for better placement in an appropriate research opportunity.
  2. Fill out the major sections of the UROP application and send a draft to Prof. Fernández (contact information below). Major sections of the UROP application include:
    • Project Overview
    • Personal Role and Responsibility
    • Goals
    • Personal Statement
  3. The ESI will confirm a UROP supervisor and approve final UROP application.
  4. Candidate will then upload a final UROP application to the UROP web site at: http://uaap.mit.edu/research-exploration/urop/options/urop-find-projects-apply

All applicants should also follow the procedure outlined on the UROP web site which aligns with the process outlined above. Please direct all questions to Prof. Fernández.

Prerequisites: No prerequisites

Relevant URL: https://environmentalsolutions.mit.edu/wp-content/uploads/2018/10/ESIAgenda_rev.-10-2018.pdf

Contact: John E. Fernandez: fernande@mit.edu


1/10/19

Department/Lab/Center: Media Lab

Faculty Supervisor: Alex `Sandy’ Pentland

Project Title: Experimental investigation of Collective Intelligence

Project Description: Collective intelligence is believed to underlay the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective intelligence. How are accurate shared beliefs formed in groups of fallible individuals? An exciting area to investigate is the use of online experimentation for answering questions about both: 1) the individual decision mechanisms people use; and 2) the properties and dynamics of those mechanisms in the aggregate. We are interested in developing an online experiment to examine the effects of network topology, social influence, repeated interactions, and reputation, on the emergence of collective intelligence.

What you will do: implement real-time and interactive Human+Bot online experiments .. mostly front-end work!

Skills you SHOULD have already: React.js/Javascript/HTML/CSS

Skills you will learn/hone: experimental design, data analysis, theories of collective intelligence.

Pluses: HCI experience, amazon mechanical turk, meteor.js

Contact: Send Abdullah Almaatouq <amaatouq@mit.edu> a short description of your background and your resume.


1/10/19

IAP/Spring

UROP Department, Lab or Center: Sloan School of Management (Course 15)

MIT Faculty Supervisor Name: Professor Stuart Madnick

Project Title: Research on Cybersecurity Risk Quantification Methodologies

Project Description: Project will involve, collecting and analyzing data on cyber risk and working with models for assessing risk. Some data sets exist, others will be created through various data collection means including discussions with C-level executives.  Early work will also include literature review and summary of existing approaches. Additional work will be done analyzing risk models and making improvements on approaches to assessing risk and choosing tasks to reduce risk in organization. Thesis level work is also available.

These projects can enhance your abilities in perform literature search, qualitative research, and data analysis skills. Additional understanding of cyber risk metrics and risk management in organizations. Selected candidate(s) are expected to join the projects immediately.

Prerequisites: Required skills include attention to details, as well as excellent reading, writing, and communication skills. Familiarity with risk-based analysis and cybersecurity is a plus but not required. We are particularly interested in working with motivated and organized students who are committed to doing research.

Relevant URL: You will be working with Cybersecurity at MIT Sloan (https://cams.mit.edu)

Contact: Michael Siegel: msiegel@mit.edu


1/10/19

IAP/Spring

UROP Department, Lab or Center: Sloan School of Management (Course 15)

MIT Faculty Supervisor Name: Claudia Steinwender

Project Title: All Aboard: Trade, Port Development and its Aggregate Impact on Cities

Project Description: Our world is as globalized as never before in history: An enormous amount of goods is shipped around the globe every day.  One important factor that has enabled this degree of globalization is the container revolution, because this has reduced the cost of handling goods at ports dramatically. Containerization has also changed the global landscape of ports, however: After WW2, the "capitals of the world" like New York and London used to be the world's most important ports. Nowadays, secondary cities that specialize in port services like Rotterdam or Shanghai have become the largest maritime trading hubs.

In this project we develop a theoretical model of cities and ports that trade with each other to study how the specialization of cities into providing port services has affected the economic development of cities around the world. This model is a structural general equilibrium model of economic geography, and we are looking for a RA proficient in Matlab to help us solve the non-linear system of equations that constitute the equilibrium of the model. You will work closely with faculty at Sloan, Columbia, and CREI.

Prerequisites: The candidate needs to have excellent skills in Matlab, especially with solving systems of non-linear equations. In the application, please provide a detailed description of your Matlab experience, which projects you have been working on, and which functions and toolboxes you are familiar with.

Contact: Claudia Steinwender: csteinwe@mit.edu


1/10/19

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Leslie Norford

Project Title: Mesoscale Energy Modeling for City Regions (MeEnMo): surrogate building energy models  and image segmentation for solar energy

Project Description: The Leventhal Center for Advanced Urbanism and the Building Technology Program in the Department of Architecture are collaborating to develop a mesoscale city-region energy simulation platform that integrates energy demand, supply and storage. The platform will also allow simulation of future climate scenarios. To achieve integration across multiple domains and between multiple data sources and to provide relevant climate simulations will require a flexible data-structure and robust data-processing algorithms to sort, classify, and/or generate surrogate models from a wide range of data sources.

We seek a UROP student during IAP and/or the spring term to utilize machine learning techniques to develop two models: 1) a surrogate model for energy demands under various climatic conditions and daily patterns, and 2) a universally applicable image segmentation model to help estimate roof-top and open-space solar potential. The surrogate model will allow for rapid calculation of energy demand from multiple building types in several density arrangements and under various levels of technology adoption. The image segmentation model will take in high resolution satellite data and use computer vision techniques to first identify various features (buildings, land-uses, etc.), and second, and more critically, to classify and measure open-space and roof surface areas and orientations.

The UROP will develop and test multiple machine learning techniques to find the most accurate models, including but not limited to linear classifiers, support vector machines, and decision trees. This position is perfect for students with experience in machine learning who want to apply their knowledge to issues of sustainable development.

Prerequisites for surrogate model:

  • Strong Python skills with evidence of independent problem solving and algorithm development.
  • Experience using energy simulation models (EnergyPlus) including either UMI or Honeybee/Ladybug.
  • Experience using data-science/machine learning with the ability to independently pursue/test multiple techniques

Prerequisites for image segmentation model:

  • Strong Python skills with evidence of independent problem solving and algorithm development.
  • Experience using computer vision / machine learning techniques to analyze and classify imagery (e.g. OpenCV, SciKit Learn)

Contact: Leslie Norford: lnorford@mit.edu


1/10/19

IAP/Spring

UROP Department, Lab or Center: Architecture (Course 4)

MIT Faculty Supervisor Name: Leslie Norford

Project Title: Mesoscale Energy Modeling for City Regions (MeEnMo): weather classification

Project Description: The Leventhal Center for Advanced Urbanism and the Building Technology Program in the Department of Architecture are collaborating to develop a mesoscale city-region energy simulation platform that integrates energy demand, supply and storage. The platform will also allow simulation of future climate scenarios. To achieve integration across multiple domains and between multiple data sources and to provide relevant climate simulations will require a flexible data-structure and robust data-processing algorithms to sort, classify, and/or generate surrogate models from a wide range of data sources.

We seek a UROP student during IAP and/or the spring term to classify and discretize weather data into a representative sub-set of day-types utilizing k-means clustering techniques. This will allow the synthetic generation and testing of extreme and probabilistic future climatic conditions. This UROP will also help investigate the day-type data structure using existing Python libraries (Pandas, Numpy, etc.).

Prerequisites: Strong Python skills with evidence of independent problem solving and algorithm development.

Contact: Leslie Norford: lnorford@mit.edu


1/10/19

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Ramesh Raskar

Project Title: Distributed & Collaborative ML

Project Description: We are looking for motivated UROPs at Camera Culture research group in MIT Media Lab. The projects would involve deep learning, optimization, statistics, and some coding with PyTorch/TensorFlow/Keras Matlab. The projects are well-defined and scoped out and an ideal candidate should be able to proactively contribute with periodic updates. Work around adjacent problem areas is also encouraged. Both hands-on experimental projects and/or theoretical opportunities exist.

Desired background: Coding fluency/research mindset.

Contact: Maggie Cohen: cohenm@media.mit.edu. Please include your resume in your application.


1/10/19

IAP/Spring

UROP Department, Lab or Center: Plasma Science and Fusion Center (PSFC)

MIT Faculty Supervisor Name: Earl Marmar

Project Title: Design and Development of User Interface for Data Management Project

Project Description: Modern science generates large complicated heterogeneous collections of data. In order to effectively exploit these data, researchers must be able to find relevant data, and enough of its associated metadata to understand it and put it into context. This problem exists across a wide range of research domains and is ripe for a general solution.

Existing ventures address these issues using ad-hoc purpose-built tools. These tools explicitly represent the data relationships by embedding them in their data storage mechanisms and in their applications. While producing useful tools, these approaches tend to be difficult to extend and data relationships are not necessarily traversable symmetrically.

We are building a general system for navigational metadata. The relationships between data and between annotations and data are stored as first class objects in the system. They can be viewed as instances drawn from a small set of graph types. General purpose programs can be written which allow users explore these graphs and gain insights into their data. This process of data navigation, successive inclusion and filtering of objects, provides powerful paradigm for data exploration.

The student will work on a project dealing with annotation and data relationships in science applications. Information on the project is available at https://ndm.mit.edu/.

Skills needed: javascript, VUEjs, basic GIT source code management, docker. The ideal candidate is a good javascript front end developer capable to work on a VUEjs based SPA.

Relevant URL: https://ndm.mit.edu/

Contact: Joshua Stillerman: jas@psfc.mit.edu


1/10/19

IAP/Spring

UROP Department, Lab or Center: Center for Transportation & Logistics (CTL)

MIT Faculty Supervisor Name: Alexander Rothkopf

Project Title: Geospatial Data Visualization of Disasters and Emergency Response Network Design Options

Project Description: CTL's Humanitarian Supply Chain Lab collaborates with national and international emergency response organizations to evaluate and improve their response capabilities and reduce negative impact of sudden onset disasters on the population. In this UROP project the candidate supports CTL researchers in developing and automating tools to visualize disaster portfolio data and emergency response network configurations. The results of this work will directly be used in collaborations with our partners to improve their emergency response networks.

Prerequisites: The successful candidate has prior experience in data management and data manipulation in python and can work with geospatial data in python or tableau.  S/he is interested in accessible visualization to a broader audience.

Contact: Alexander Rothkopf: rothkopf@mit.edu


1/10/19

Term: Spring/Summer

UROP Department, Lab or Center: The MIT Energy Initiative (MITEI)

MIT Faculty Supervisor Name: Christopher Knittel

Project Title: Uncovering the discrepancies in estimates of compliance costs with fuel economy standards between the 2016 TAR and the 2018 NPRM

Project Description: Following the Energy Independence and Security Act of 2007, the Environmental Protection Agency (EPA) and National Highway Traffic Safety Administration (NHTSA) set fuel standards to achieve a fleet-wide fuel economy of 35 mpg by 2016, and a projected 27 to 55 miles per gallon between 2012 and 2025. As part of the 2017-2025 standards issued by the agencies in 2012, the EPA and NHTSA were required to conduct a midterm review of the fuel economy improvements affecting model years 2022-2025. The 2016 technical assessment report (TAR) concluded that these 2022-2025 standards were technologically feasible, and that benefits far exceeded costs.

Recently, the Trump Administration in a 2018 Notice of Proposed Rulemaking (NPRM) finds that the costs of these Obama-era standards now exceed benefits, and proposes to freeze them at model year 2020 levels through 2025.

In relation to the 2016 TAR, while the 2018 NPRM reports overall lower benefits, the major substantial change comes from the estimation of the costs of compliance with the standards. Specifically, compliance costs with the corporate average fuel economy (CAFE) standard are 2.8 times higher in the 2018 proposal than the 2016 NHTSA analysis (and, in turn, even in the 2016, NHTSA projected compliance costs 2.6 times higher than the simulation models used by the EPA). And compliance costs with the GHG emissions standard are 7.5 times higher in the 2018 proposal, an increase from $35 billion in the 2016 TAR to $253 billion in the 2018 NPRM.

The purpose of this project is to uncover the discrepancies that drive the differences in estimates of compliance costs with fuel economy standards between 2016 TAR and the 2018 NPRM.

Full description of the project proposal can be found here: http://ceepr.mit.edu/files/UROP/2018-11-6-Knittel-UROP-fuel-economy-standards.pdf

Interested students should send their resumes and a writing sample, if available, to ceepr@mit.edu.

Prerequisites : We are looking for UROP students seeking credit or a stipend (paid position), with a strong preference for students who would like to commit to at least 2 semesters of work, or more (starting date is flexible). Ideal candidates would be responsible, motivated and detail-oriented and would be able to spend at least 8-10 hours a week on the project.  The project may also involve writing reports for the topic above and other projects that may fit. Basic programming skills, especially in R and Matlab  are required.

Relevant URL: http://ceepr.mit.edu/research/projects/UROP

Contact: Tony Tran (ceepr@mit.edu)


1/7/19

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Charles Leiserson

Project Title: Scalable graph learning for anti-money laundering

Project Description: We've seen deep learning do *remarkable* things on Euclidean data - audio, images, video. Not so much yet on graph data, until very recently. Graph data is structurally different; it's all about relationships between data. Think social networks, gene expression networks, knowledge graphs, you name it - graphs are all around us. In finance we can think about trading, hedging, and asset management, supply chain finance and optimization, lending and securitization. Each of these can use graphs to capture relationships and interactions between different types of entities, often with a time series component, and often in a dynamic setting. The problem is, deep learning on graph data is extremely difficult computationally due to the combinatorial complexity and nonlinearity inherent to graphs of any meaningful size and density. And it's precisely the information hidden in that complexity that makes graph data so interesting and important. Recently we've seen a rapid and exciting acceleration of work on graph convolutional networks, or GCN's, with special attention to the question of scaling. With GCNs, we begin with certain attributes to describe the nodes and edges, and we use convolutions over the graph to pull out the hidden properties and patterns. This is called node embedding and the objective is to achieve a better vector representation of each entity. In laymens’ terms, you can think of each node asking the age-old question, “Who am I?”  It’s really an existential question with infinite complexity, but we need a vector of finite length. So we have to bound the model or it’s going to take a prohibitively long time, and find a way do so without sacrificing accuracy. This is the challenge of scalability. Earlier this year at ICLR, Jie Chen and Tengfei Ma presented a new method called FastGCN. This work was a big step forward on scalability. FastGCN was able to beat previous speed benchmarks by two orders of magnitude. It does so by using a variant method for importance sampling and by performing integral transformations in node embedding to account for node inter-dependency. Building on FastGCN, we're now exploring how we can further advance graph deep learning, and finance presents some interesting use cases; for example, the problem of anti-money laundering.

Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150,000 people since 2006; upwards of 700,000 people per year are ``exported'' in a human trafficking industry enslaving an estimated 40 million people. These nefarious industries rely on sophisticated money laundering schemes to operate. Despite tremendous resources dedicated to anti-money laundering (AML) only a tiny fraction of illicit activity is prevented. The research community can help.

We will explore baseline models as well as advanced graph-based models, which use more discriminative features resulting from the graph structure for identifying outliers. Objectives include: 1) Understand the characteristics of financial graphs (in what aspects are they different from graphs in other domains?); 2) Establish the baseline performance of commonly used machine learning tools for financial fraud detection; 3) Explore feature generation approaches (graph deep learning-based) to improving over the baselines.

Follow the link in Relevant URLs to the MIT-IBM Watson AI Lab for further application questions.

Prerequisites: Python, experience with TensorFlow and/or PyTorch, graph theory and analytics, data science (sourcing, wrangling, and analysis of data; this is a real world project - you won't be given a perfect data set)

Relevant URL: https://goo.gl/forms/MeDtpAAPVxqerOVy1

Contact: Mark Weber: mrweber@mit.edu


1/7/19

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Luca Daniel

Project Title: Adversarial robustness in deep learning: design and interpretability

Project Description: This project will aim to have a deep understanding on the mechanism of adversarial attacks to fool deep neural networks under imperceptible perturbations. Specifically, our goals include a) generalizing adversarial attacks from time-independent domain to time-varying domain (e.g. from image classification to video classification), b) analyzing how the adversarial effect evolves over network layers, and c) designing effective defense methods for adversarial attacks in the time-varying domain.

UROP will help RSM in attack/defense algorithm design, software implementation and performance evaluation. The final deliverable will be software tool and publication.

Follow link in Relevant URLs to the MIT-IBM Watson AI Lab for further application questions.

Prerequisites: Deep learning knowledge and programming skills

Relevant URL: https://goo.gl/forms/MeDtpAAPVxqerOVy1

Contact: Sijia Liu: sijia.liu@ibm.com


1/7/19

IAP/Spring

Multiple Openings

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Peter Shor

Project #1: Mathematics of AI and Machine Learning

Project Description: Mathematical aspect of machine learning and AI. This is a  research oriented project to (semi)rigorously establish empirically observed features in AI and ML. Ideally the work will eventually lead to a publication (but is not a requirement). There can be software that is developed for the investigation. There are many empirical observations in AI and Machine Learning that need quantification and mathematical formulation for their domain (in)applicability to be carved out. We will start filling this gap.

Prerequisites: Linear Algebra. Coding (Matlab or Python or C). Some probability theory. Being able to read papers in the field.

Relevant URL: https://goo.gl/forms/MeDtpAAPVxqerOVy1

___________

Project #2: Quantum AI: How can quantum enable AI? Short depth quantum

Project Description: Quantum AI: How can quantum enable AI? Short depth quantum.  This is a research oriented project to investigate the power of short-depth quantum circuits with a focus on quantum AI. Ideally the work will eventually lead to a publication. What can AI do for Quantum? What can Quantum do for AI? The latter has been more elusive so far. There are quantum algorithms that speed up linear algebraic subroutines such as solving linear systems. We will research the space between quantum and standard AI to quantum enable AI algorithms to run faster or process larger data. There will be a special emphasis on the utility of near term quantum computers such as the ones IBM currently has. These are short-depth quantum circuits.

Prerequisites: Linear Algebra. Coding (Matlab or Python or C). Being able to read quantum computing papers.

Relevant URLs: https://goo.gl/forms/MeDtpAAPVxqerOVy1

Contact: Ramis Movassagh: ramis@us.ibm.com


1/7/19

IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Tommi S. Jaakkola

Project Title: Diagnosing undesirable behavior in complex natural language processing models

Project Description: Over the past few years, complex models such as neural networks have dramatically pushed the state-of-the-art in various tasks in natural language processing, from machine translation to parsing. Yet, the same complexity that makes these models powerful often masks "undesirable" behavior, such as lack of generalization, bias propagation, or other flaws. Interpretability thus holds the key to diagnose such behaviors and to satisfy reliability, fairness or privacy criteria. In this project, we will investigate the use of interpretability methods for diagnosing flaws in NLP models, experiment with current methods and develop new ones. We are particularly interested in the problem of gender bias in NLP, which has recently attracted significant interest from the scientific community due to its prevalence and impact.

Specific responsibilities include: literature review on recent work on interpretability for natural language processing, implementing current methods, developing and implementing new ones, implementing user-friendly visualization pipelines.

Prerequisites:

  • Demonstrated experience in machine learning (e.g., 6.036, 6.867 or similar)
  • Proficiency in python
  • Proficiency in machine learning software (e.g., scikit-learn, pytorch, tensorflow)
  • BONUS: Experience with visualization tools (D3.js, plotly,  Processing.js)

Relevant URL: https://people.csail.mit.edu/davidam/projects/interpretable_ml.html

Contact: If you are interested in this position, please send an email to David Alvarez-Melis (dalvmel@mit.edu) including a CV + transcript, and indicating:

  • a short description of why you are interested in working on the project
  • the number of hours you would work on the project per week
  • whether you are seeking a UROP for credit or for pay

1/7/19

IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Tommi S. Jaakkola

Project Title: Finding correspondences in time-varying data with optimal transport

Project Description: The problem of analyzing the evolution of objects (such as point clouds, or more generally, probability distributions) through time arises in many contexts, from diachronic linguistics (the study of language change over time) to dynamic scene understanding in self-driving cars. Optimal transport is a flexible mathematical toolkit for comparing distributions, and has been extensively used for object comparison and matching. Yet, its application to dynamic settings has been limited. In this project, you will investigate the use of optimal transport for these settings. The project has opportunities in both theoretical or practical aspects, and relative freedom in terms of the type of application, which can be informed by the student's interests.

Specific responsibilities include: literature review on foundational (optimal transport) and practical (application) topics, implementation of baseline approaches, development of an optimization framework for the problem at hand, computational implementation of said framework and experimental comparison.

Relevant URL: https://people.csail.mit.edu/davidam/projects/structured_ot.html

Contact: If you are interested in this position, please send an email to David Alvarez-Melis (dalvmel@mit.edu) including a CV + transcript, and indicating:

  • a short description of why you are interested in working on the project
  • the number of hours you would work on the project per week
  • whether you are seeking a UROP for credit or for pay

1/7/19

Spring

UROP Department, Lab or Center: Aeronautics and Astronautics (Course 16)

MIT Faculty Supervisor Name: Dava Newman

Project Title: A software platform to explain local to global impacts of climate change

Project Description: When we learn about climate change, we learn about global climate change. How average sea levels are expected to rise, storms become more extreme, temperatures become even warmer. But often what we care most about are the impacts of climate change on our  own communities – what will summer in my neighborhood feel like? Will I be affected by extreme droughts or flooding? This project seeks to build/leverage a software platform that can answer these kinds of questions, interpolating results from global models to allow curious citizens to understand how the consequences of global climate change will impact their local community.

This project has two main components: (1) organizing the available data into a labeled, organized database and (2) designing a user interface to enable intuitive access to this data. Our team has already identified a number of global datasets on historical and projected climate data from US and European government agencies, but has yet to organize these disparate sources into a form that enables users to search all of them and access location-specific results. Once organized into a useful resource, there are a few options to effectively convey the information. You could visualize the local results and display them in an app or webpage, create a conversational agent using Amazon Alexa or Google Home, or leverage other platforms (i.e., Resource Watch) to engage users with how climate change impacts their local area.

You will be responsible for software development in this project with mentoring from a graduate student researcher. To support your work, you’ll have access to the design and engineering expertise of Prof. Dava Newman, graduate student researchers from a number of departments, and industry partners in our larger research group. This is an opportunity to see a project through many stages of development and could lead to further collaborations as our research team deploys the final product and tests its effectiveness at spurring action on Earth’s Systems: oceans and climate change implementing space data.

Required skills: programming experience (python preferred), familiarity with large online databases, interest in UI/UX design, (AI, NLP DeepLearning, or API experience, a plus)

Contact: Julia Milton: jmilton@mit.edu


1/7/19

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Peter Szolovits



Project Title: Neural Question Generation

Project Description: Automatically generating questions (and answers) for machine comprehension to support several information extraction problems in unstructured patient records. Design a notion of "question-worthy" parts of the patient note and incorporate it into a hierarchical neural model. Investigate if generation can span passages in a note or go across notes. Evaluate approach on freely i2b2 and MIMIC datasets and see if it can augment the existing emrQA dataset (http://aclweb.org/anthology/D18-1258) The UROP will 1) Usable baseline models for question generation from text, and 2) 1+some innovation w.r.t multi-passage or multi-document answering resulting in a paper. New research that will facilitate a future academic paper as well as training a more robust model for QA on patient records.

Please follow link in Relevant URLs for additional MIT-IBM Watson AI Lab application questions.

Prerequisites: Familiarity with basics of NLP (especially question answering), ability to code in python with either tensorflow or pytorch, general curiosity to learn new things and try them.

Relevant URLs: https://goo.gl/forms/MeDtpAAPVxqerOVy1

Contact: Preethi Raghavan: praghav@us.ibm.com


1/7/19

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Tamara Broderick

Project Title: Quantifying predictive uncertainty through Bayesian deep learning

Project Description: As a powerful tool for modeling complex functions, neural networks have revolutionized many application domains.  However, standard deep models are also poorly calibrated: they do not provide reliable estimates of confidence in predictions and are often overly confident when making incorrect predictions.  Reliable confidence estimates are important for a variety of situations, including identifying situations when a model should not be trusted as well as making risk-sensitive decisions when uncertainties are high.



Bayesian neural networks (BNNs), which explicitly track the uncertainty in the neural network parameters, promise to provide more reliable confidence estimates. Unfortunately, as the number of model parameters in a neural network is huge, inferring accurate BNN posteriors is challenging. Techniques that approximate the BNN posterior often produce poor uncertainty estimates. While there are recent results which suggest BNNs in certain regimes behave like more intuitive stochastic processes (in particular, Gaussian processes), it is unclear if any of the plethora of recently proposed techniques for approximately learning BNNs actually achieve this idealized behavior. The objective of this project is to understand and empirically evaluate whether and under what circumstances modern BNN inference techniques recover the Gaussian process limit.



The UROP will compare algorithms commonly used for learning BNNs and comparing to a variety of alternative machine learning algorithms -- e.g. Gaussian process models, linear models, modern interpretable list models, etc.  This project would involve both implementing and when available reusing (including techniques developed by us) BNN inference algorithms as well as metrics for evaluating the quality of uncertainties produced by the learned models.  Particular data sets of interest include electronic health records and medical data. The UROP will work closely with our researchers to analyze the regimes under which the algorithms work and fail. The final deliverable will be in the form of python code. Additional extensions (if desired) could include designing new approximate inference algorithms for BNNs that rectify shortcomings of existing approaches.

Follow link in Relevant URLs for additional MIT-IBM Watson AI Lab application questions.

Prerequisites: Python programming, experience with either TensorFlow or PyTorch. Have taken 6.867 or equivalent introductory machine learning coursework.  Some familiarity with approximate inference techniques will be useful, but not strictly necessary; there will be opportunities to pick up these skills as part of the UROP experience.

Relevant URL: https://goo.gl/forms/MeDtpAAPVxqerOVy1

Contact: If you are interested in working on this project, contact Soumya Ghosh: ghoshso@us.ibm.com. Note that we will require some preliminary tasks before we can formally accept a UROP on the project (see immediately below). Feel free to contact us if you have any questions about the tasks.

  • Describe your relevant machine learning background (e.g. coursework, internships, etc) by email
  • Either use a standard package (glmnet or R, etc) or code a method for LASSO
  • Run LASSO on a data set of your choice
  • Report the results and why they are interesting (in text by email and/or in person)

1/7/19

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Patrick Jaillet

Project Title: Collective Bayesian Optimization for Decentralized Multi-Task Learning

Project Description: In many industrial settings, practical problems of interest often need to be solved concurrently by different, independent problem-solving systems and many of these problems are usually grounded in related domains, which at least share strong domain-specific similarities. In such scenarios, taking advantages of related problem-solving knowledge acquired by others to improve one's problem-solving efficacy is essential to achieve the first step towards human-like intelligence: collaborative and communicable AI systems that automatically learn from shared experience and generalizing the learned knowledge to solve related tasks more efficiently. In traditional machine learning literature, this idea has been extensively investigated in the context of transfer learning or domain adaptation where one takes advantage of available and abundant data from related domains to improve the predictive accuracy in a target task. This body of literature is, however, restricted to the domain of predictive analytics and centralized learning where data are centralized in a single machine and predictive model's architectures are revealed publicly, which ascertains the feasibility of knowledge transfer.

However, in many practical settings, centralizing data and publicizing model architectures are not desirable due to privacy concerns. In fact, even having a centralized machine only to handle communication across local machines is also not preferable as it would expose a single choke point for operational failure and place severe computational bottlenecks on the central machine's storage, communication and computation capacities. Furthermore, in the specific context of black-box optimization, which forms the core of most ML systems (e.g., hyper-parameter tuning in predictive analytic systems and/or optimal action planning in prescriptive analytic systems etc.), problem-specific data are rarely available prior to the onset of the optimization process and not surprisingly, most existing optimizing approaches (ranging from traditional to black-box paradigms) have neglected this re-usability aspect of past experience. In other words, problem-solving systems in such settings do not communicate and do not improve their efficacy with experience. In this regard, it is worth mentioning that in many practical applications, involving time sensitive actions and/or high cost of evaluations, ignoring the knowledge gained from related optimization experiences can lead to deleterious computational overheads in the re-exploration of similar parameter spaces.

With the above, any successful problem-solving systems is expected to operate independently and collaboratively via peer-to-peer communication. In essence, such systems need to be capable of consolidating, streaming and circulating their individual observational experiences between optimization tasks and among themselves. Therefore, the main objective of this proposal is to develop novel computational capabilities featuring efficient collective knowledge (expressed in the form of learned models of recurring optimization patterns) assimilation across different systems (that can communicate in peer-to-peer fashion) for improved efficacy in problem-solving. Specifically, this research focuses on a class of decentralized, communicable black-box optimization algorithms which are designed to optimize multiple non-differentiable learning objectives concurrently and collaboratively via communication.

The result of this research project is expected to impact a wide range of industrial applications that align with the specific goal of enhancing productivity in problem-solving and autonomous decision-making through human-like knowledge transfer across problems, which forms the backbone of many AI-empowered decision support systems.

A general-purpose collective black-box optimization framework, together with associated software prototypes, realizing the aforementioned notion of knowledge transfer in the context of decentralized multi-task learning. In particular, novel algorithms will be developed to extract, represent, communicate and assimilate (shared) data generated during the course of optimization efficiently and accurately. The work done in this project (preferably over the combined IAP and Spring terms) will also be oriented towards submissions to premier AI/ML conferences.

What the UROP will do: The UROP will be working along our team of several research staff members (RSMs) specialized in AI/ML, with ample opportunities for learning and guidance. 

Specific workload includes (but not limited to):

  • Exploring the existing, relevant literature to the research topic (with guidance from the RSMs) -- the goal is to help the UROP familiarize him/her to the literature and essentially, understand the developed framework
  • Participating in weekly discussion with our team of researchers and (if desired) help extending our developed approaches and/or developing new approaches
  • Implementing our developed framework of Collective Bayesian Optimization and setting up empirical studies to demonstrate its efficiency
  • Packaging the work done into a submission to a AI/ML conference (e.g., AAAI/ICML/NIPS)

Prerequisites:

  • Programming: Python, C++ and experience with TensorFlow and Keras
  • Background: non-parametric statistics, optimization and basic knowledge in probabilistic machine learning.

Relevant URL: https://goo.gl/forms/MeDtpAAPVxqerOVy1

Contact: Nghia Hoang: nghiaht@ibm.com


1/4/19

Spring/Summer

UROP Department, Lab or Center: Brain and Cognitive Sciences (Course 9)

MIT Faculty Supervisor Name: Pawan Sinha

Project Title: Humanitarian/Scientific initiative for treating curable blindness and studying visual development

Project Description: We are looking for UROP/s that can help us in our dual goal humanitarian/scientific initiative to provide treatment to curably blind children and study their subsequent visual development using a range of behavioral, imaging and computational methods. In particular, we have been studying the development of object and face perception as well as the development of drawing skills following recovery from prolonged congenital blindness.  We currently have data that needs to be sorted, annotated and analyzed.

A self motivated and interested UROP will also have opportunities to become involved in defining new research questions, designing and preparing new experiments, and joining in on many other exciting aspects of this project.

In this study, we focus on basic face recognition and drawing abilities, particularly in the hours and days following treatment for congenital blindness from cataracts.  We have video data recorded from the first hours and days immediately upon sight onset, as well as longitudinal data on standardized tests of face and expression identification (both behavioral and eye-tracking).  The student will be asked to sort through this data, which will include watching and annotating videos, performing data sorting and analysis with excel, and then will be supported with performing data analysis using varying techniques.  As needed, students will also be expected to perform literature search and help me with conceptualizing new and appropriate ways of analyzing the behavioral and eye-tracking data.  The students will be supported and mentored directly by me (a staff research scientist), but are expected to independently manage their time and meet deadlines.  This position may be available for direct pay or credit.

Prerequisites: The ideal candidate/s will have a strong interest in visual development and behavioral studies, and should be interested in contributing to both the scientific and humanitarian aspects of our project.

Significant advantage for background in experimental psychology and/or cognitive science.  Creativity and patience is a must!  Research requires first and foremost the ability to remain motivated and problem solve despite ambiguities, and that is particularly true with out project.

We are looking for someone who is self motivated (though I will always be around to guide and help) and ideally with at least a potential interest in returning to or continuing work with the lab.  Many of our UROP's stay on for multiple semesters to follow their projects through, and we love that!

Knowledge of hindi and ability to translate is a plus but not a must.  We also have a project that involved fMRI data analysis - if you are interested in this and in particularly have any fMRI analysis experience, we want to meet you!  Finally, basic programming and stats skills, or willingness to self-teach, are a huge plus.

Please note - ability to invest at least 6-9 hours a week is a must.  Please make sure that you are indeed comfortable with this and are not over-reaching with your schedule.

Relevant URL: www.projectprakash.org

Contact: Sharon Gilad-Gutnick: sharongu@mit.edu


1/4/19

Spring

Multiple Opening 

UROP Department, Lab or Center: Mechanical Engineering (Course 2)

MIT Faculty Supervisor Name: A John Hart

Project #1: Optimization of Printed Sensors for On Site Soil Nutrient Analysis

Project Description: We are developing an on site soil nutrient analysis and management system to provide small holding farmers with actionable soil health information.  The soil nutrient analysis system will enable the accurate measurement of pH, nitrate, phosphate, and potassium - providing critical information for the farmer to efficiently utilize fertilizer inputs and maximize crop yields. We are seeking a student to investigate the impact of different parameters on the performance of our sensors, optimizing the designs for a pilot test in India.  The project will involve fabricating sensors and characterizing performance using electrochemical, profilometry, and microscopy techniques.

Prerequisites: The project is open to all relevant engineering majors. Enthusiasm and willingness to learn new experimental techniques are most valued.  Interest or experience in fabrication, electrochemistry, and/or design for resource constrained environments is great.

___________

Project #2: Development of Electrochemical Reader for On Site Soil Nutrient Analysis

Project Description: We are developing an on site soil nutrient analysis and management system to provide small holding farmers with actionable soil health information.  The soil nutrient analysis system will enable the accurate measurement of pH, nitrate, phosphate, and potassium - providing critical information for the farmer to efficiently utilize fertilizer inputs and maximize crop yields. We are seeking a student to help with the development of a point of use electrochemical reader, designing the system for a pilot test in India.  The project will involve the continued development and refinement of a microcontroller-based system for making open circuit potential measurements, and integrating the system into a user friendly, portable device.

Prerequisites: The project is open to all relevant engineering majors. Interest and experience in electronics hardware fabrication, pcb design, arduino programming, and/or design for resource constrained environments is great.

Contact: If you are interested, please email Michael Arnold (mjarnold@mit.edu) with your resume/CV. Work hours are flexible and can be discussed in a pre-meeting. There is a possibility of continuing working in subsequent semester(s).


1/4/19

Spring/Summer

UROP Department, Lab or Center: Materials Science and Engineering (Course 3)

MIT Faculty Supervisor Name: Michael Cima

Project Title: Estimating pathogenic steatosis progression in fatty liver disease via machine vision

Project Description: The Cima lab at the Koch Institute specializes in translational research that applies an interdisciplinary approach towards diagnostics and drug delivery. The research group includes post-docs, grad students, and undergrads spanning many departments including EECS, MechE, DMSE, BCS, and HST.

This project aims to develop a noninvasive diagnostic for fatty liver disease. Nonalcoholic fatty liver disease (NAFLD) and it's more advanced form, nonalcoholic steatohepatitis (NASH), have reached epidemic scales and now affect 20% of adults in the US. This disease can progress to liver failure, liver cancer, cirrhosis, and death. Early diagnosis and intervention can prevent progression, but we currently lack a low cost, non invasive tool.

This project will involve applying the latest computer vision and machine learning techniques to identify the fraction of steatosis (fat accumulation) in liver histology images. This will be used as a gold standard against which to test a non-invasive diagnostic of NAFLD and NASH.

You will be working with two EECS PhD students in the lab with experience in machine learning and computer vision. You will be able to start algorithm development immediately as we currently have an extensive set of images collected by our team. There are many existing software packages that will form the basis for your work and provide a starting point.

If you would like to apply, please email a resume/CV and a short description of your interests, goals, and how this UROP would help you realize those goals. Please include your availability for the week of January 28 for an interview and lab tour.

Prerequisites:

  • Proficiency with scientific programming languages (eg Python, MATLAB)
  • Familiarity with image processing and computer vision preferred
  • Prior research or work experience in a fast paced, independent environment preferred

Relevant URL: https://cima-lab.mit.edu/

Contact: Ashvin Bashyam: ashvin@mit.edu


1/4/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Ramesh Raskar

Project Title: Machine Learning for Capturing the Appearance of Objects

Project Description: This project explores the use of machine learning for capturing object properties such as surface reflection from images. Faster caption of object material properties let machines understand more about world. We are looking for motivated students who are interested in building a deep learning pipeline for described task. In this project, you will learn about machine learning, computational imaging and graphics.

Prerequisites: Previous experience with machine learning implementation is preferable but not required. We look for self-driven students.

Contact: Maggie Cohen: cohenm@media.mit.edu


1/2/19

IAP

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Danielle Wood



Project Title: Paraffin Wax Centrifuge Experiment

Project Description: The Space Enabled Research Group advances justice in Earth’s complex systems using designs enabled by space.  Space technology contributes to the United Nations’ Sustainable Development Goals via communication, Earth observation, positioning, microgravity research, spinoffs and basic research.  Space Enabled uses six methods to apply space technology to development, including art, design, social science, complex systems modeling, satellite engineering and data science.

The research group is conducting experimental work considering novel ways of forming non-toxic rocket propellants, in particular paraffin wax (common candle wax).  The student would develop and refine an experimental procedure for melting paraffin wax and forming it into annular rocket fuel grain geometries.  The student would also be programming an Arduino to control an oil-based centrifuge experiment.

Prerequisites: Demonstrated knowledge of experimental processes with a particular focus on safety related to physical experiments/mechanical hardware.  Preference for students with Arduino experience and mechanical/aerospace/electrical engineering backgrounds.

Relevant URL: spaceenabled.media.mit.edu

Contact: Javier Stober: stober@mit.edu


1/2/19

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Danielle Wood

Project Title: Exploring the Dynamics of Learning and Decision-Making to Apply Space Technology in Support of Sustainable Development

Project Description: The Space Enabled Research Group advances justice in Earth’s complex systems using designs enabled by space. Space Enabled uses six research methods to apply space technology to sustainable development: design, art, social science, complex systems modeling, satellite engineering and data science. In this project, Space Enabled emphasizes the use of design thinking and social science to understand the experiences of participants in projects that apply space technology in support of sustainable development. Specifically, the project uses methods from anthropology, sociology, history and economics to explore social aspects of technology projects. Several case study technology projects are examined in Malaysia, Vietnam, Thailand, Benin and Tunisia; in each case study an organization applies space technology to respond to a local need in a new way. All the case studies examine projects that use space technology because this is an increasingly feasible opportunity for countries in every region of the world. The case studies ask questions such as: 1) What sociotechnical imaginaries does the community hold about the impact of space technology on their community? 2) What learning processes are used to learn new technology? In this project, students will analyze data for existing case studies and help prepare to collect data for future case studies. Students will also participate in literature review on the topics of cultural impacts of technological learning and the influence of technology on decision making.

Prerequisites: We seek students with a combined interest in social science and science or technology. We prefer students with knowledge in any of the following fields: sociology, anthropology, economics, political science, organizational theory, history, urban studies and planning, international studies, science and technology studies. Students that have experience coding qualitative interview data and performing academic literature reviews are preferred. In addition, we seek students interested in the topic of sustainable development.

Relevant URL: spaceenabled.media.mit.edu

Contact: Javier Stober: stober@mit.edu


1/2/19

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: V. Michael Bove

Project Title: Looking Sideways creative online exploration tool

Project Description: During IAP and Spring semester in the Media Lab’s Object-Based Media group, we’ll be continuing to develop computational design tools that embrace ambiguity and serendipity, vital for technologies used early in the creative process.  We’ll be refining the Looking Sideways inspiration exploration tool (http://sideways.media.mit.edu/), an online browsing tool that seeks to provoke unexpected inspiration and guide pathways to new ideas through providing users with a selection of semi-randomly chosen, loosely related, diverse sources from art, design, history and literature for every search query.  We’ll be carrying out user studies and integrating that feedback into modifications of the tools, e.g. refining the UI and building new features.  There is also opportunity to work on the design(human)design tool (http://reframe.media.mit.edu/), and help integrate these tools into a projection mapping table so that they can be viewed in a more immersive environment.

Prerequisites:

  • Strong JavaScript skills (ideally with knowledge of D3 visualization library)
  • Front end web development

Contact: Philippa Mothersill: pip@mit.edu


1/2/19

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Joseph A. Paradiso

Project Title: Hands-on fashion design for wearable smart bands

Project Description: The Responsive Environments group at the Media Lab is looking for an undergraduate researcher to participate in a project to make our wearable smart bands look fashionable. Devices are fabricated in the lab using advanced techniques that allow electronics to be stretchable in a rubber substrate. The smart bands can be customised to fit the wrist, chest or head to track electrical activity, motion or heartbeat. One of our current issues is that the device substrate is transparent and many users don't like seeing the electronics inside. We are looking for a highly motivated and creative student to make these smart bands look stylish with colorful and eye-catching patterns. You will not only be responsible for the designs but also for fabricating your own prototypes as we will show you how to manipulate rubber, pigments and stencils.

Prerequisites: We expect you to have some kind of experience in art or fashion, even if it's just as a hobby. It would be ideal if you are also familiar with computer tools that can to convert your sketch contours into a graphic image format (e.g. DXF file extension), which will be necessary to fabricate the stencils. If interested in the position, please contact me with your portfolio or pictures of your art/fashion works. The position is available for IAP, IAP + Spring, or just Spring.

Contact: Carlos Nunez: cnunez@mit.edu


1/2/19

IAP/Spring

UROP Department, Lab or Center: Urban Studies and Planning (Course 11)

MIT Faculty Supervisor Name: Prof. Alan Berger

Project Title: The American Suburbs Project

Project Description: How suburban is America? What percent of American’s live in low-density, car dependent suburbs versus dense, walkable communities? And how many reside in rural areas yet still commute into the city center for work? Surprisingly these questions have not be adequately answered at the neighborhood level. A new research project at the Leventhal Center for Advanced Urbanism aims to clearly answer these questions and share the results through an online platform. The American Suburbs Project will study and define the scope of America’s suburban population over the last decade using a widely published method previously applied to Canada and Australia. The results will therefore allow researchers to understand differences in metropolitan growth between American cities, as well as comparatively across all three countries. The outcomes of this study have broad implications for housing, transportation, and land-use policies, as well as the transition to more sustainable and resilient cities.

The UROP(s) will assist in the translation of an existing Excel-based manual workflow into a scalable and parameterized computational workflow.

UROP will:

  • Download and pre-process US census and landcover datasets
  • Perform spatial statistics based on demographics, transportation mode, and housing characteristics, to identify distinct types of urban and suburban regions
  • Validate results, under supervision, using Google Earth and other third-party datasets
  • Prepare the results for broad communication in the form of static and interactive web maps and a peer-reviewed article.

Learning + Opportunity: We welcome applicants from all courses interested in the project. Students in course 11.6 or course 4 or 11 with an emphasis on spatial analysis are especially encouraged to apply. This is a great opportunity to improve spatial analysis skills under the guidance of research associates at the LCAU and in the context of a real-world research project.

Prerequisites: The candidate should be proficient in either Python or R programming environments (Python preferred). Experience working with spatial data, GIS data, and US Census data is preferred, as is familiarity with Javascript for front-end web development.

Contact: Pru Robinson: pru@mit.edu


1/2/19

IAP

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Danielle Wood

Project Title: Exploratory Design for Technology Initiatives Supporting Urban Youth Outreach in Colombia

Project Description: The Space Enabled Research Group advances justice in Earth’s complex systems using designs enabled by space.  Space technology contributes to the United Nations’ Sustainable Development Goals via satellite communication, Earth observation, positioning, microgravity research, spinoffs and research capacity.  Space Enabled uses six research methods to apply space technology to development, including art, design, social science, complex systems modeling, satellite engineering and data science.

The city government of Cali, Colombia has invited the Space Enabled Research Group to explore how we can apply our six research methods to city initiatives that harness information technology to foster civic pride, entrepreneurship and educational opportunities for youth. We seek an undergraduate student to contribute to this design process with the government of Cali, along with faculty, graduate students and research staff of Space Enabled. Space Enabled will support an undergraduate student to apply for the Davis Projects for Peace Fellowship with the goal of implementing a project during summer 2019 based on the goals of the city government of Cali.

Prerequisites: Strong preference for students with proficiency in Spanish, demonstrated interest in sustainable development, and technical background in geographic information systems, computer science, sociology, social psychology, urban studies, art, or design.

Relevant URL: spaceenabled.media.mit.edu

Contact: Javier Stober: stober@mit.edu


12/26/18

IAP/Spring

UROP Department, Lab or Center: Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Adam Albright

Project Title: Experimental Studies in Phonetics and Phonology

Project Description: In this project, we run a set of experiments that test hypotheses regarding speakers’ implicit knowledge of linguistic sound patterns, and the ways in which this knowledge is acquired when learning a new language. Students will participate in creating experimental materials, such as artificial words, using recording and sound editing equipment. In addition, students will participate in running the experiments online and/or in person. Appropriate training in phonetics, sound editing and working with experimental software will be given as needed.

Prerequisites: Basic knowledge of phonetics is desirable but not required.

Contact: Daniel Asherov: asherov@mit.edu


12/26/18

IAP/Spring

UROP Department, Lab or Center: Center for Transportation & Logistics (CTL)

MIT Faculty Supervisor Name: Jarrod Goentzel

Project Title: Ugandan Farmer Market Engagement Analysis

Project Description: This UROP will assist with analyzing quantitative and qualitative datasets from the US Agency for International Development. In cooperation with USAID, we recently conducted a survey in Uganda from which we intend to derive both statistical and descriptive narrative results by analyzing household data to understand market access for smallholder farmers. In addition to the survey, we have access to large quantities of time series data on traders and farmers collected by another USAID activity operating in Uganda. This UROP position will involve manipulating and analyzing data, preferably using Excel, R, or Python. The applicant should have experience with some data analysis platform. They will also perform some other qualitative and quantitative research Position may also include writing up of analysis and becoming familiar with USAID literature. There is the potential for research/paper credit depending on the length/scope of contribution. This work will eventually be used to support policy decisions at USAID.

Prerequisites: This UROP position will involve manipulating and analyzing data, preferably using Excel, R, or Python. The applicant should have experience with some data analysis platform.

Relevant URL: http://humanitarian.mit.edu/projects/feed-the-future-uganda

Contact: Tim Russell: trussell@mit.edu


12/26/18

IAP/Spring

UROP Department, Lab or Center: Comparative Media Studies (21 CMS)

MIT Faculty Supervisor Name: Justin Reich

Project Title: Analyzing text communication for emotion and conversational strategies in practice spaces (simulations)

Project Description: Our multidisciplinary laboratory--the MIT Teaching Systems Lab (TSL)-- is comprised of engineers, learning designers, learning scientists, and social science researchers. We are  looking for students with an interest in education, learning science, and quantitative and AI research. The project focuses on how participants communicate during difficult conversations, use of conversational strategies, and how to support reflection of novice teachers using AI and Natural Language Process (NLP). Students will work closely with TSL researchers to collect dispositional information about participants to examine convergent validity of text measures with validated measures. Possible student tasks include:

Developing text classifiers that measure emotional expression and/or conversational strategies Conducting pilot studies during playtests at TSL (which occur every 2 months) Analyzing how text analytics relate to validated measures Students will work closely with TSL researchers familiar with the detailed goals of the project and will gain hands-on experience in qualitative and mixed-methods research. Sponsored research funding is available.

Prerequisites: Some previous experience using natural language process (NLP) (e.g., NLTK in Python). Some previous experience training text classifiers (e.g., scikit-learn in Python). Some previous experience doing regression analysis (e.g., logistic regressions in R).Please provide details on any prerequisites or skills required for this UROP

Relevant URL: tsl.mit.edu

Contact: Please send a resume and cover letter to Garron Hillaire (garron@mit.edu). In a brief cover letter, please describe how your experience or interests would be a good match for the project(s). Also, please indicate whether you are looking for a IAP or spring semester position (or both), and whether you are seeking a UROP for credit or for pay.


12/26/18

IAP/Spring

UROP Department, Lab or Center: Comparative Media Studies (21 CMS)

MIT Faculty Supervisor Name: Edward Schiappa

Project Title: Representing Student Protest of the 1960s in University Histories: A Narrative Synthesis

Project Description: This project examines how official university historians represent student protest during the 1960s as documented in university-sanctioned “history of the university” books.  The method of analysis is known as a “narrative synthesis” (Smith, 1989) approach to rhetorical analysis that examines the plot, central character, and values expressed within a set of related narratives.  We will look for dominant narratives in these historical accounts.  It is likely that we will find two--the first can be described as the “assault on the university” narrative, which tends to demonize student protest; the second can be described as “students seek to change the world” narrative, which portrays student protest sympathetically.  Our analysis will note the relationship between the primary historiographical methods employed by the historian (archival versus oral histories, for example), and the likelihood of one or the other dominant narrative.

Prerequisites: This project would be of interest to any student interested in history, political science, literature, nonfiction writing, or media studies.  Basic library research skills needed and a willingness to learn how to do basic rhetorical analysis of historical narratives.  Training provided.

Contact: Edward Schiappa: schiappa@mit.edu


12/26/18

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Fadel Adib

Project Title: Smart phone-based AI for Detecting Food Contaminants

Project Description: The goal of this project is to develop a smartphone-based AI to detect food contaminants. The project builds on a recent technology from the MIT Media Lab which uses AI to detect food and quality safety.

Here is a link to recent coverage by TechCrunch describing our technology: https://techcrunch.com/2018/11/14/rfid-stickers-could-signal-contaminated-food/

Responsibilities: This UROP position involves the following tasks:

  1. Developing a smartphone-based interface — on iOS or Android — to interact with the technology
  2. Develop a simple client-server architecture to control the device and output classification result on the screen
  3. Learn about different AI algorithms used for food detection
  4. Run experiments with different types of contaminants and detect contamination using the smartphone-based AI

Prerequisites:

  • iOS or android development
  • client-server programming
  • background in algorithms

Contact: Unsoo Hal: unsoo@mit.edu


12/26/18

Spring

UROP Department, Lab or Center: Linguistics and Philosophy (Course 24)

MIT Faculty Supervisor Name: Martin Hackl

Project Title: Language Processing Research

Project Description: We investigate the nature of human language by studying adult language processing. The research interweaves current linguistic theory and empirical work. The current research areas include quantified statements, focus operators, and presuppositions. Your work will involve (i) experimental design, (ii) stimulus creation, (iii) interaction with experimental software platforms (coding or and dealing with data). It might also involve (iv) assisting with or proctoring in-person behavioral experiments.

The ideal UROP will be enthusiastic about engaging with behavioral research, interested in linguistics and language development, and looking for a chance to learn new skills.

The UROP's main goals will be: engagement with cutting edge theoretical developments in linguistics, and acquiring hands-on experience with behavioral research.

Prerequisites: There are no pre-requisites for this UROP assignment. Having taken 24.900 is preferred but not required. Skill with programming or image editing a plus.

Contact: Please contact us with a resume or CV. Wellesley Students are welcome and encouraged to apply.  Leo Rosenstein: leaena@mit.edu


12/26/18

IAP/Spring/Summer

UROP Department, Lab or Center: Brain and Cognitive Sciences (Course 9)

MIT Faculty Supervisor Name: Josh McDermott

Project Title: Cross-cultural studies of music perception

Project Description: We plan to conduct a set of experiments comparing how individuals in Western and Amazonian societies perceive musical pitch and consonance. Previous studies in our lab have provided evidence, for example, that consonance is not universally perceived as more pleasant than dissonance– the Tsimane’, a native society of horticulturalist-foragers in the Bolivian Amazon, do not demonstrate an aesthetic preference for consonance over dissonance. This UROP opportunity will involve running studies relating to music perception, preparing for a data collection trip to Bolivia during the summer. The UROP will likely be asked to join this trip. This is an interdisciplinary project combining psychophysics, auditory psychology, and music cognition.

Prerequisites:

  1. Fluent Spanish (Mandatory)
  2. Comfort in MATLAB
  3. An interest in working with human participants
  4. Outdoor/travel experience and comfort
  5. Must be available for Spring and Summer 2019, in particular, must be available during the month of August for a data collection trip to Bolivia

Relevant URL: https://www.nature.com/articles/nature18635

Contact: Malinda McPherson: mjmcp@mit.edu


12/26/18

Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Joichi Ito

Project Title: Community Biotechnology

Project Description: Come join the Media Laboratory’s Community Biotechnology Initiative, led by Dr. David Sun Kong! As living technologies proliferate, how do we ensure that communities, diverse socioeconomically, culturally, and creatively, are able to not only experience its benefits, but are also active participants and agents of change?

Through this Initiative, we: (i) organize communities, including a growing network of ‘community biology’ labs as a new form of collective intelligence for the life sciences; (ii) work with partners at MIT Sloan School and Harvard Business School to develop frameworks for community and crowd-based innovation; and (iii) develop open-source, accessible tools for biotechnology.

We seek four UROP students to join our Spring team. Projects include:

Technology

  • Research robust, low-cost solutions versions of cutting edge bio-technologies
  • Develop co-culture microbial systems
  • Design and fabricate an open-source “Lab-in-a-Box,” a portable biotechnology laboratory
  • Research electroporation of microbes in salty environments.

Collective Intelligence & Crowd-Based Innovation

  • Apply collective intelligence frameworks to communities and corporations in the life sciences in collaboration with the MIT Center for Collective Intelligence;
  • Develop and launch community contests & challenges for technology innovation in collaboration with the Laboratory for Science and Innovation at Harvard (LISH)

Community Organizing

  • Advance ‘Metafluidics,’ an open repository of fluidics and bio-hardware with a community of 1.5k+ users;
  • Organize the third annual Global Community Biology Summit, a gathering of community and independent bio labs around the world held at the Media Lab, Fall 2019.

Prerequisites:

  • a desire to utilize biotechnology as a means to create a more just, equitable society
  • curiosity, enthusiasm, and a passion for learning
  • Juniors are preferred for electronic board design and hardware prototyping
  • Juniors/sophomores are preferred for wetlab work

Experience & Skills (preferred but optional):

  • basic prototyping wet lab experience, including molecular and micro-biology techniques
  • electronic board design and hardware
  • microfluidic design and fabrication
  • experience with fabrication technologies and hardware development, with biology applications a plus
  • proficiency with wordpress and/or other web development platforms and tools
  • social science experience related to collective intelligence or community/crowd-based innovation
  • community organizing experience
  • user experience design

Relevant URL: https://www.media.mit.edu/groups/community-bio/overview/

Contact: David Kong: dkong@mit.edu


12/26/18

IAP/Spring/Summer

UROP Department, Lab or Center: Materials Science and Engineering (Course 3)

MIT Faculty Supervisor Name: Lionel Kimerling

Project Title: Building a blended learning curriculum for workforce development in integrated photonics

Project Description: Are you interested in gaining experience in the lab while creating educational materials that will be incorporated in online courses and blended learning bootcamps? Integrated photonics is an emerging technology that combines optical and electronic semiconductor devices into a photonic integrated circuit (PIC) chip. We are creating an online and blended learning curriculum to meet the growing demand for education and workforce training in this new field. In this UROP position you will perform lab experiments and help create tools for education and workforce training in photonic device characterization and testing. This work will be closely guided by a post-doc and technician; you will create educational documentation using the knowledge you gain in the lab, and also help to design an online virtual test bench.

Prerequisites: Interest in learning about the emerging field of integrated photonics

Contact: Anu Agarwal: anu@mit.edu


12/26/18

IAP/Spring

UROP Department, Lab or Center: Mechanical Engineering (Course 2)

MIT Faculty Supervisor Name: Pierre Lermusiaux

Project Title: Mobile Applications for Ocean Predictions and Marine Autonomy.

Project Description: Many of us check the weather on our smartphones (or ask Alexa) to decide what to wear today or what to do the next weekend. Smart devices are becoming ubiquitous and they track many human activities to help us make better decisions. However, out in the deep seas, we don’t yet have the luxury of having ocean information at our fingertips. Fortunately, this situation is changing and we at the MSEAS group have recently developed capabilities for smart autonomous monitoring and modeling of our oceans. Specifically, we have developed a suite of science- and engineering-based data-driven stochastic prediction systems to forecast ocean conditions, optimal ship routes, probable fishing zones, and pollution.

We now seek a UROP to develop web and mobile applications that scale and deploy our modeling results to our end-users. The proposed work involves developing front-end (web and mobile interface) and back-end (distributed and cloud) technologies. For the front-end, we plan to develop an iOS, android, and web apps. For the back-end, we will employ xarray, dask and kubernetes to process our modeling results and serve it to the front-end. Candidates are expected to possess excellent programming skills (e.g. python), and an interest in working with big data, big compute and AI for environmental conservation. Experience with creating mock-ups or user-interface design is another advantage.

Relevant URLs: see our list of projects in https://superurop-apply.mit.edu/searches/searches.tcl?dept=meche and http://mseas.mit.edu/

Contact: Pierre Lermusiaux: pierrel@mit.edu


12/26/18

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Pierre Lermusiaux

Project Title: Machine Learning of Uncertain Dynamical Models

Project Description: Our understanding of many physical systems in the world is imperfect. This is why it is important to account for the uncertainty when making predictions of the weather, ocean currents, river pollution, or complex fluid flows, to name a few. In this particular project, the dynamical equations governing the physical system are either not well known or unknown, but some data time series are available. To recover or discover the dynamical equations, Bayesian methods combining model prediction and data assimilation may be used. Another approach uses deep neural networks to make predictions of the system evolution without learning, or while learning, the model equations.

This UROP will work on the various new methods developed by our group to learn the governing model equations and functional formulations from sparse data in a Bayesian sense. We have also been utilizing more traditional machine learning techniques such as Gaussian processes and neural networks to learn the model dynamics through data driven observations / sampling. The UROP will work with our team to further develop and apply Bayesian and deep learning methods.

Relevant URLs: see our list of projects in https://superurop-apply.mit.edu/searches/searches.tcl?dept=meche and http://mseas.mit.edu/

Contact: Pierre Lermusiaux: pierrel@mit.edu


12/18/18

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Ekene Ijeoma

Project Title: Poetic Justice

Project Description: Poetic Justice is a new group at MIT Media Lab which creates artworks that extend our perceptions and expose the social-political systems affecting us as individuals. Through producing interdisciplinary works that embody our human conditions, we engage people in social transformation; imagining and realizing change.

Some of our first projects include:

The Green Project, a series of publications and interactive installations developed through storytelling and mapping workshops that reimagine the Negro Motorist Green Book for “traveling while Black” in today's “New Jim Crows”. The series aspires to create visibility, accountability, and solidarity for the state of Black mobility and safety today as WEB Du Bois did for the state of Black life in America in 1900.

The Scream Project, a series of publications and interactive installations which revive the Teotihuacan folklore/ritual of women practicing catharsis in the pyramids to contemporary urban spaces.

Look Up, an app-based public artwork which prompts city-goers to look up at every intersection in the US. It was released in 2015 as an Android Live Wallpaper/background app available only on Android Play and in NYC and received a lot of press. It's free, doesn't require a SIM card, and is low power. Summer 2019 we’re relaunching the app for both Android and iOS and expanding it nationwide. Applicants for Look Up should be experienced or interested in at least one of the following:

  • Developing iOS and/or Android apps
  • Developing data structures/backend systems in Python/Javascript

Prerequisites: Poetic Justice is looking for applicants who are passionate about breaking down the complexities of social issues and building up visibility, accountability, and solidarity around them. Applicants should be interested or experienced in at least one of the following: interaction design, information design, architectural design, industrial design, graphic design, music, performance, film, software engineering, hardware/electrical/mechanical engineering, writing/journalism, storytelling, and community organizing/activism.

Relevant URL: https://www.media.mit.edu/groups/poetic-justice/overview/

Contact: Rebecca Cuscaden: cuscaden@media.mit.edu


12/18/18

IAP/Spring

UROP Department, Lab or Center: Biology (Course 7)

MIT Faculty Supervisor Name: David Housman

Project Title: Genetic modifiers of Huntington’s disease

Project Description: David Housman helped pioneer the discovery of the genetic marker for Huntington’s disease (HD). The Housman lab now studies how the rest of the human genome controls the age at which a patient with the Huntingtin mutation becomes symptomatic for the disease. We have identified other genetic markers that modify HD age of onset by using extensive resources of patient samples and clinical data collected over decades from the world’s largest HD family in Venezuela. We are now characterizing dysfunction of the proteins encoded by these genetic variants in HD patient samples and mice models. Further, we aim to discover the role of modifier variants in the pathology of the disease by genetic manipulation in mice models and patient-derived induced pluripotent stem cells differentiated into neurons. Understanding how these genetic variants alter the course of the disease will distinguish the molecular pathways that are most capable of modulating Huntington’s onset. By going from genetic to molecular insights, we hope to target these modifier pathways to develop protective therapies capable of slowing HD pathology. The objective of this UROP will be to assist in all aspects in identifying and characterizing genetic modifiers of HD. This work will be closely guided by a post-doc and technician in addition to offering opportunity for a more independent project upon demonstration of research prowess. This work will be primarily experimental / wet lab-based but there is later opportunity for computational bioinformatics work with large genetic and next-generation sequencing datasets on a high performance computing cluster.

Prerequisites: Previous experience with molecular biology techniques is preferred but not required. For additional computing work, programming experience and familiarity with Linux would be preferred. Commitment beyond one term is required (IAP & Spring or ideally for a year).

Contact: Christopher Ng: cwng@mit.edu


12/18/18

IAP/Spring

UROP Department, Lab or Center: Aeronautics and Astronautics (Course 16)

MIT Faculty Supervisor Name: Prof. Jonathan How

Project Title: Robust Pedestrian Tracking and Scene Understanding for Self-driving Cars

Project Description: Our goal is to improve the safety of autonomous driving in urban areas, specifically in busy streets and crowded intersections. A crucial component of this is understanding and predicting pedestrian intent, modeling which needs a large amount of training data from the real world . The goal of this UROP is to work on pedestrian tracking and classification algorithms which can be run on our portable data collection platform consisting of cameras and a Velodyne mounted on top of a tripod. The first step in extracting pedestrian trajectories from raw camera and Lidar data is to detect/classify pedestrians and track them while in the field of view of the data collection platform. Understanding the environment contexts is helpful to predict pedestrian accurately. Our goal is to combine the  scene segmentation package (ICNet) with pedestrian tracking algorithm to improve the accuracy of pedestrian motion predication. This UROP would require working with a graduate student/postdoc on improving the existing pedestrian detection tracking algorithms for better, more robust trajectory extraction as well as scene understanding. UROPs are expected to devote 25-30 hrs/week during IAP and 15-20  hrs/week during Spring semester

Prerequisites:

  • Passionate about robotics/autonomous driving
  • Comfortable with Linux and installing packages
  • Basic experience with ROS, C++, and Python
  • Knowledge of vision based object detection and classification and tracking, scene segmentation algorithms would be helpful, but is not required

Contact: Golnaz Habibi: golnaz@mit.edu


12/18/18

IAP/Spring

UROP Department, Lab or Center: Sociotechnical Systems Research Center (SSRC)

MIT Faculty Supervisor Name: Prof. Stuart Madnick

Project Title: Cyberspace Operations Functional Taxonomy: Automated Tools for Development and Evolution of a Task Catalog

Project Description: Cyberspace Operations (CO) Tasks/Actions can be undertaken for Offence, Defense, and Security purposes. This project aims to develop a full spectrum catalog of CO activities/tasks in the form of a functionally decomposed taxonomy. The structure of the taxonomy is based on analogy to the functional taxonomy used in traditional physical (air, land, maritime) domains of warfare as expressed in unclassified military doctrine publications. CO tasks will be derived both from cybersecurity standards and other cyber domain documents and from reasoning by analogy to the physical domains.

Potential Student Projects:

1) Crawl a corpus of doctrine documents to extract a sample of tasks and identify the taxonomy structure for physical domains. While reviewing documents, identify rules that could be used for automated support for task extraction.

2) Review cyber domain documents to extract sample tasks and make analogies to the physical domain tasks and taxonomy. Identify rules that could be used for automating analogy.

3) Develop a knowledge base to store and access sample tasks, derived tasks, and taxonomy structure. Also make use of off-the-shelf technology to give users access to view and explore the taxonomy as it develops.

4) Proof-of-concept experiments with Deep Learning/Natural Language Processing methods (such as Tensorflow and PyTorch) to categorize and insert a list of tasks into the cyber domain taxonomy.

5) Proof-of-concept experiments with Defeasible Logic Programming (such as Coherent Knowledge’s Ergo AI tools) rules-based methods for deriving and maintaining the cyber domain taxonomy and knowledge graph.

6) Review public domain accounts of successful or attempted cyber attacks and infer what tasks attackers and defenders used. Relate the tasks found to the cyber domain task taxonomy.

Each student project will include a focused literature review and experiments with applying lessons from the literature. Research will be conducted individually and collaboratively with other students as part of the project team.

Research Group: Sociotechnical Systems Research Center and Cybersecurity at MIT Sloan Interdisciplinary Consortium for Improving Critical Infrastructure Cybersecurity

Prerequisites: An interest in in-depth understanding of CyberOperations.

For projects 1, 2, and 6, the ability to read and understand technical and military documents and to extract and organize key knowledge found.

For projects 3, 4, and 5, experience in programming with languages such as Python and Prolog and with data representation standards such as JSON. Please provide details on any prerequisites or skills required for this UROP

Relevant URL: https://cams.mit.edu/

Contact: Allen Moulton, Research Scientist, Project Manager: amoulton@mit.edu


12/18/18

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Andrew Lippman

Project Title: imagePlace

Project Description: Are you intrigued by collaborative and emergent web behaviors?  Great, so are we!

The Viral Communications group of the MediaLab is looking for UROPs to join us and to become part of the imagePlace project, a web app akin to Reddit's r/Place that acts as a contributory community website that rallies people under a common cause.

The current iteration of this project can be found here https://votomosaic.media.mit.edu/ and was implemented to motivate people to vote for the US Midterm elections in November. The next iteration will be a more open-ended experience where the tiles of the mosaic can be individually selected and interacted upon. Including media like adding video, sound or text could be nice additions.

Join us because:

  • you will work on a visual, people-centered project
  • you will have a supervisor (second year grad student) that is present and committed to guiding you throughout the project's development
  • you can learn about design and UX
  • you will work in an enthusiastic and supportive team
  • you will get to know the legendary Media Lab
  • you are in amazingly close proximity to said Lab's foodcam (where all the free food ends at). This typically means you don't need to pay for lunch 3/5 days of the week.

Prerequisites:

  • really good grasp of Python, particularly the image processing libraries (ex PIL but not limited to that)
  • really good javascript skills, preferably React.
  • experience in interacting with third-party APIs
  • experience working with AWS infrastructure
  • experience with web sockets (ex socket.io)
  • high commitment and availability during IAP and spring

Relevant URL: https://votomosaic.media.mit.edu/

Contact: Kalli Retzepi: kalli@mit.edu


12/17/18

IAP/Spring

UROP Department, Lab or Center: Political Science (Course 17)

MIT Faculty Supervisor Name: In Song Kim

Project Title: Data Science/Machine Learning to Study Money and Politics

Project Description: We study money-in-politics in the U.S. Our large scale public database, available through https://www.lobbyview.org/, allows researchers and the public to effectively learn how lobbying affects political outcomes. Our interdisciplinary team utilizes various state-of-the-art machine learning and data science methods to analyze high-dimensional social science data.

Prerequisites: The candidate is expected to have a good knowledge of python and development in general (python 3.X, git, github pull request, tickets, tests, PEP 8). Knowledge of SQL (CTE, aggregate, windows function) / database world (PostgreSQL 11) is a big plus. Knowing Natural Language Processing would also help.

UROPs will work approximately 10 hours per week for either or both of the IAP 2019 and Spring 2019 terms. 

Relevant URL: https://www.lobbyview.org/

Contact: Interested applicants should send a short statement of interest indicating preferred start date, along with a resume/CV to Rémi Cura at remi.cura@gmail.com.


12/17/18

Spring

UROP Department, Lab or Center: Linguistics and Philosophy (Course 24)

MIT Faculty Supervisor Name: Martin Hackl

Project Title: Child Language Development Research

Project Description: We investigate the nature of human language, by studying immature language in the child (the development of language). The research interweaves current linguistic theory and empirical work. The current research areas include quantified statements, focus operators, and presuppositions.

Your work will involve:

  1. running experiments with children (mainly 3-6 years old),
  2. data entering and assisting with interpretation,
  3. interacting with day cares and parents for consent. It might also involve
  4. assistance in experimental design and preparation of experimental materials. The ideal UROP will be enthusiastic about engaging with children, interested in linguistics and language development, and looking for a chance to learn new skills. The UROP's main goals will be: engagement with cutting edge theoretical developments in language acquisition and acquiring hands-on experience with behavioral research with children. Please contact us with a resume or CV. Wellesley Students are welcome and encouraged to apply.

Due to travel schedules during IAP, most interviews will have to be conducted at the beginning or at the end of January. Please contact us as soon as possible if you are interested in a position for this Spring semester.

Prerequisites: There are no pre-requisites for this UROP assignment. Having taken 24.900 is preferred but not required. Given that the work is mainly about interaction with children and keeping them engaged in the experiments, you will have to be very good at playing with kids in a structured way. You must also have large chunks of time available during weekdays in either mornings or afternoons, in order to run experiments.

Contact: Leo Rosenstein: leaena@mit.edu


12/14/18

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Dr. Roger Mark

Project Title: Building Data Structure Visualization Tools for Electronic Health Records

Project Description: The Laboratory for Computational Physiology hosts two large open electronic health record (EHR) databases consisting of medical information from patients admitted to intensive care units (ICUs). This project involves working with the recently released eICU Collaborative Research Database, which is a multicenter database of ICU admissions collected from hospitals throughout the USA. eICU-CRD is a relational database, but contains additional structure which is not fully exploited and can be difficult to navigate, particularly for those who are unfamiliar with the database or creating database queries.

This project would aim at exposing vital information from this currently hidden structure to allow others to find important data and concepts, and building an interface to allow people to easily navigate where these data and concepts are located within the database.

This project may be of interest in or looking to learn about databases, medical informatics, using machine learning in healthcare, and building interfaces for data science products.

Prerequisites: Applicants should be interested in learning about electronic health systems, or medical research, be details orientated and know a programming language. Knowledge of databases is an asset, but not necessary, as are: serialized data structures such as JSON, the ability to work independently, and troubleshoot.  Applicants who know Git, Python, R and/or Javascript are preferred, but applicants who are enthusiastic about the project should apply regardless of their experience.

We are willing to consider applicants for IAP and IAP/Spring.

Contact: Dr. Jesse D. Raffa: jraffa@mit.edu


12/14/18

Multiple Openings

UROP Department, Lab or Center: Mechanical Engineering (Course 2)

MIT Faculty Supervisor Name: Professor Wim M. van Rees

Project 1: Computational investigation on the effects of curvature in flapping foils

Project Description: The vast majority of fish and birds rely on the use of flapping airfoils for their locomotion. A closer observation at their fins shows a combination of heave and pitch movements with a dynamic adjustment of the cross-section curvature which is not yet fully understood. The investigation of the impact of this active shape control mechanism in the flow patterns and net forces constitutes the main goal of this project. A parametric study covering numerical simulations of multiple 2D airfoil shapes under a broad range of deformation regimes will be conducted using our in-house Navier-Stokes equations solver. It is written in modern C++ and exploits multi-resolution grids and shared-memory parallelism. A follow-up project would use evolutionary optimization algorithms in order to optimize the parameters and geometry for maximum performance, and investigate the three-dimensional flow structures using our in-house 3D Navier-Stokes solver.

Who should apply?: If you are excited about computational fluid dynamics and the interface between computer science, mathematics, and engineering, then this project will provide an opportunity to get familiar with initializing, running, and analyzing simulations.

If you are interested, please apply or email with your resume/CV. There is one UROP position for Spring 2019. Work hours are flexible and can be discussed in a pre-meeting. There is a possibility of continuing working in subsequent semester(s).

Prerequisites: Need to have a basic understanding of fluid dynamics. Familiarity with basic numerical methods (finite difference, timestepping, elliptic solvers) and C++ is appreciated.

Contact: David Fernández-Gutiérrez: davidfg@mit.edu

------------------------------------------

Project 2: Helicity in a fluid flow: vortex rings and loops

Project Description: Helicity is a scalar quantity that measures the degree of intertwining and linking of vortex lines inside a fluid flow. A circular vortex ring, such as a smoke ring, does not necessarily have helicity, but once the ring has undulations or other asymmetries, local helicity dynamics can play an important role in the evolution of the flow. Using simulations of the three-dimensional Navier-Stokes equations on a parallel compute cluster, we aim to investigate those helicity dynamics for some basic vortical flows. The goal of this project is to design initial conditions for a helical flow, implement those in our in-house code, simulate it, and visualize/analyze the results. As such, this project combines fluid dynamics, mathematics, and computational science.

Who should apply?: If you are excited about computational fluid dynamics and understanding the fundamental building blocks of vortical flows, then this project will provide an opportunity to get familiar with some of these aspects.

If you are interested, please apply or email with your resume/CV. There is one UROP position for Spring 2019. Work hours are flexible and can be discussed in a pre-meeting. There is a possibility of continuing working in subsequent semester(s).

Prerequisites: Need to have a basic understanding of vector field calculus and fluid dynamics. Familiarity with basic numerical methods (finite difference, timestepping, elliptic solvers) is appreciated. Our in-house solver is written in Fortran-90, so experience with that is a plus - but the language and code is easy to pick up.

Contact: Geoff Fox: gfox@mit.edu


12/14/18

IAP

UROP Department, Lab or Center: Sloan School of Management (Course 15)

MIT Faculty Supervisor Name: David Thesmar

Project Title: Measuring investor demand for Corporate Social Responsibility through experiments.

Project Description: The objective of this research is to measure the premium that investors are willing to pay for “well-behaving” corporations. The recent years have experienced a dramatic shift on financial markets towards more “corporate responsibility”. Investors do not only care about profits anymore: Financial institutions and money managers become more and more insistent that companies behave in a prosocial way, i.e. take better care of their employees and community, promote diversity, be more environmentally conscious, etc. A natural implication of this view is that prosocial behavior should have price, in the sense that the stocks of socially responsible companies should trade at a premium. 

Our objective in this study is to use a lab experiment to evaluate the value of corporate social responsibility. This experiment is based on the following insight: Imagine the stock of a company is worth $10. Now, the company commits to give 10% of its profits to a charity. Purely selfish investors will drive the stock down to $9. But if investors are truly altruistic, the price may still be P>$9. In this case, P-9 is the dollar value of prosocial behavior from the perspective of the shareholder. The experiment consists in placing participants in such a situation, eliciting their value of P, and deriving the stock-market value of prosocial behavior. Various other experimental conditions can be explored: looking at aversion to anti-social behavior (does it lead to a discount of the same size), looking at delegation (am I managing my own money or another person’s money?), etc. 

Scope: In order to implement this experiment, we need to set up a web interface in order to collect the results and process payments. The student will need to set up a robust, clear and effective interface. This requires basic coding skills, as well as a willingness to engage in the project and provide suggestions to improve the design. The student work closely with the professors working on this project. The student will be implicated in data collection and analysis of the results.

Prerequisites: A working knowledge of Java or Python is important. The student needs to be committed to deliver the interface at the end of IAP. To have fun, students need to be interested in behavioral economics and psychology. 

Contact: David Thesmar (thesmar@mit.edu)


12/14/18

IAP/Spring/Summer

UROP Department, Lab or Center: Aeronautics and Astronautics (Course 16)

MIT Faculty Supervisor Name: Edward Crawley

Project Title: Machine-Learning in Space with the Largest Satellite Operator

Project Description: The System Architecture Lab in AeroAstro is looking for two UROPs to develop the front-end visualization for a machine learning dynamic resource management tool for communication satellites. The UROPs will be part of an exciting sponsored research project with SES, the largest satellite operator worldwide. The project aims to develop an autonomous dynamic resource allocation tool for their new fleet of flexible high-throughput satellites.

We have done some initial modeling and developed some AI algorithms. However, due to the high complexity of the problem, we need help with an interactive visualization of the AI results. The front-end will also be used to communicate with the sponsor, so the UROPs will be key team members. There will be a clearly defined work package for each UROP.

Prerequisites:

  • Excited to work on a real and impactful project in close collaboration with the company
  • Creativity and out of the box thinking
  • Experience in Python
  • Knowledge in RF communication is a plus

Since we also need help with setting up some infrastructure, we want to have one of the two UROPs focus more on the infrastructure, whereas the other one focuses on visualization development. Please let us know about your preference in the application.

Because we want to make you integral part of the project, we are looking for longer-term commitments with at least one day per week during Fall and Spring. Full-time availability during IAP is a plus. If you are interested in joining our team, contact us with your CV.

Contact: Markus Guerster: guerster@mit.edu


12/14/18

IAP/Spring/Summer

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Leslie Pack Kaelbling and Tomas Lozano-Perez

Project Title: Learning to reason for task and motion planning problems

Project Description: We are interested in task and motion planning problems, where the robot has to determine a sequence of low-level motions to achieve a high-level objective, such as cooking a meal. This involves planning in a complex hybrid search space where the robot has to determine a sequence of objects to manipulate, and how to manipulate them. Unfortunately, standard planning algorithms do not inherently come with the ability to improve its search efficiency with experience, and searches from scratch every time it encounters these hard planning problem instances.

Our goal is to endow the robot with the learning capability to use its planning experience, much like humans do. Specifically, this project will involve devising algorithms and representations that would guide the search of the planner for task and motion planning problems using the past planning experience.  We will use state-of-the-art machine learning techniques, such as adversarial training, policy search, or variants of Q-learning, to accomplish this objective.

Prerequisites: 6.036

Relevant URL: http://people.csail.mit.edu/beomjoon/

Contact: Beomjoon Kim: beomjoon@mit.edu


12/14/18

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Ming Dao

Project Title: Classification of red blood cells using deep convolutional neural network

Project Description: Red blood cells (RBCs) are responsible for oxygen and carbon dioxide transportation in our bodies, and it is well established that measurement of morphological abnormalities can be used for health evaluation and hematological disease diagnosis. However, automated RBC shape classification methods are still not widely used in practice. In this project, we intend to develop a robust RBC classification method using deep convolutional neural networks. Applications to specific RBC diseases such as sickle cell anemia will be explored.

Prerequisites: Must be familiar with Matlab. Prior experience with image classification and/or machine learning based on convolutional neural network will be preferred for the project.

Relevant URL: http://nanomechanics.mit.edu/publications/

Contact: Ming Dao: mingdao@mit.edu


12/14/18

IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Eric Klopfer

Project Title: TaleBlazer – Location-based Augmented Reality on Smartphones

Project Description: Interested in location-based technology? Interested in games?  Want to play with smartphones? Apply to work on TaleBlazer for pay or credit as a UROP! TaleBlazer is a location-based Augmented Reality game creation platform. Game designers build interactive games using the TaleBlazer Editor web application.  Similar to StarLogo, Scratch or App Inventor, the TaleBlazer Editor includes a blocks-based programming environment that allows the game designer to specify the game logic.

Game players use the TaleBlazer mobile application to download and play TaleBlazer games on GPS enabled phones (Android or iOS).  As the players move around the real world, they meet virtual characters or objects in the game world that the game designers have built for them.

TaleBlazer is intended for educational purposes – the players explore subject matter in a new and exciting way in a real world context. We have worked with zoos, schools, after-school clubs, etc. to design and launch various professionally developed games with science, math, and history content.  The TaleBlazer Editor can also be a valuable teaching and learning tool for student game designers, who learn programming skills and game design, while delving deeply into subject matter to create games about specific topics.

Technology: The TaleBlazer Mobile application is built via Axway Studio using Titanium, a 3rd party toolkit which allows the programmer to write a single codebase in JavaScript that is then compiled into native iOS and Android applications.  The TaleBlazer website is based on a CakePHP/MySQL backend with a JavaScript/HTML/CSS frontend.

A single semester position for the spring semester 2019 is available for pay or for credit.  Candidate may start work during IAP if desired.

Project: Map Improvements

  • Make it easier for the Game Designer to create maps at a pedestrian friendly and GPS appropriate scale
  • Make it easier for the Game Designer to port a game to a new location
  • Design a wizard to make placing agents in ported games more easily
  • Allow the Player to zoom in, pan , and rotate a map during gameplay
  • Identify and implement other usability enhancements

Prerequisites: While these positions require a strong programming background, experience with specific programming languages is not required. Availability to work majority of hours during business hours at the STEP Lab is required.

Relevant URLs: taleblazer.org, education.mit.edu

Contact: If you are interested in this position, please send an email to tea-jobs@mit.edu and include:

  • an overview of your programming experience (specific references to relevant courses and other development and programming projects would be very helpful) including any pertinent URLs
  • please specify which position(s) you are interested in
  • a summary of any previous UROP and work experience (attach a resume if you have one)
  • a short description of why you are interested in working on this project

12/14/18

IAP/Spring

UROP Department, Lab or Center: Sloan School of Management (Course 15)

MIT Faculty Supervisor Name: Professor Stuart Madnick

Project Title: Policy Research on Cybersecurity Impact on International Trade

Project Description: Overview: Cybersecurity plays an important role for the digital society. Issues of international trade policy have gained increased attention, but cybersecurity has not been a key issue for trade policy until recently. Working together with the MIT Internet Policy Research Initiative, Cybersecurity at MIT Sloan (CAMS) is working on a project to understand how cybersecurity concerns impact international trade and vice versa. As an on-going project, we already collected some related cases, developed a framework to understand the influence mechanisms between cybersecurity and international trade. Based on the preliminary research, we identified some key issues for the next two steps of this project: (1) cybersecurity impact on global financial services and (2) cyber norm development. Hence, we are looking for at least two students to join the team and work on this project in the coming year to help the policy makers and business leaders create policies and make more informed decisions related to handling cybersecurity concerns about international trade.

Two specific projects:

  1. Impact of Cybersecurity Risk on Global Financial Services In this project, we focus on the question: how do cybersecurity concerns impact global financial services? The student will work on tasks including but not limited to: collecting cases where concerns for cybersecurity have led to disruptions or banning of international financial services, such as payment systems and banking; build framework to understand the global supply chain and interdependencies for financial services; collect, analyze and compare cybersecurity related trade policies in financial service sector across different nations; draft and present reports and suggestions based on the findings.
  2. How to Develop Cybersecurity Norms to Avoid Escalating Trade Disruptions.  At its core, the Internet is based on voluntary agreements on standards for communication, e.g., TCP/IP, essentially norms of behavior. This project focuses on whether and how to develop cybersecurity behavior norms to avoid or minimize trade disruptions. A recent bilateral example was the agreement between President Obama and President Xi that neither country would use cyberattacks to gain intellectual property for commercial purposes. Goals of the project include collecting such cases; understand how international norms can be constructed at different levels: international, regional, bilateral, industrial, etc.; draw on experiences in attaining norms in other setting and times (e.g., agreement amongst most nations to not use chemical warfare after World War I); also develop examples and insights regarding what does not work; draft and present reports and recommendations based on the findings.

You would be working with the CAMS research team to study related materials and theories, collect related cases, involved interview, and draft reports to summarize the findings. The results will be presented to the CAMS research group and members.

Cybersecurity at MIT Sloan (CAMS) fills a critical need for leaders and managers of cybersecurity. Our research focuses on managerial, strategic and organizational topics. For more detail, please check: https://cams.mit.edu.

Prerequisites: Candidates ideally should have background related to cyber security policy, financial service or global supply chain. Research experiences related to case study, interview, or quantitative research is preferred. Research experiences related to system dynamics or data analysis would be a plus. Spoken English fluency and strong writing skills are required. Demonstrated potential to excel in presenting research ideas to executives is also required.

Relevant URL: https://cams.mit.edu

Contact: Keman Huang: keman@mit.edu


12/13/18

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Andrew Lippman

Project Title: Unspoken News

Project Description: The Viral Communications group of the MediaLab is looking for UROPs to join us and to become part of the Unspoken News project: we algorithmically analyse TV news to detect hidden cues beyond the spoken word to better understand presentational biases.

How something is presented can be as important as the message itself. We aim to use machine learning to model the "subcarriers of information" present in a TV newscast, including the layout of the set, the affect of the participants, the nature of the motion, and other cues. This would enable a broad-range, comprehensive analysis of how news presentation is trying to shape the public political debate. Insights in this area are of vital importance in the age of political polarisation.

Scope: A typical scene in a TV news program consists of several segments: it includes a news anchor (or several speakers in a studio), a caption, background scene and some on-screen graphics. In order to analyse the frame we first have to detect these different segments.

As part of a small team, you will work on detecting text, faces, people, on-screen graphics and extracting background information in a TV news frame. For some of these you can use Google Vision, Places and OpenPose, for others you will create custom models and training sets. During the Spring term (or starting earlier) we will work on understanding higher level characteristics of TV news such as TV set atmosphere or political bias: we will extend our training set of news which contains metadata generated by SuperGlue with characteristics you extracted during IAP and will then use deep learning to generate new high-level insights.

Perks:

  • You will work on a visual, concrete, real-world problem
  • You will work in an enthusiastic and supportive team
  • You get to watch a lot of news!
  • You will get to know the legendary MediaLab

Prerequisites:

  • Proficiency in Python (preferably incl. common scientific libraries)
  • Experience in interacting with third-party APIs
  • Familiarity with Machine Learning
  • Familiarity with Computer Vision
  • High commitment and availability during IAP

Contact: Veronika Eickhoff (eickhoff@mit.edu), David Anderton-Yang, Agata Lapedriza (agata@mit.edu)


12/13/18

Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Roy Welsch; Stan Finkelstein

Project Title: Improving Healthcare with Electronic Health Record and Synthetic Clinical Trials to Repurposev Drugs

Project Description: The aim of the project is to develop and validate methods to repurpose FDA approved drugs, drawing on concepts from statistics, data science, and machine learning, applied to a large electronic health records (EHR) dataset. The specific context of our work will be an effort to repurpose medicines that are currently FDA approved and marketed for certain conditions that can be shown to offer therapeutic value in treating significant unmet medical needs, including Alzheimer's Disease and cancer. We aim to use observational data to construct and compare cohorts of patients in a fashion that emulates clinical trials, effectively conducting “in-silico” or “synthetic trials.” For conducting this work, we are able to access the UK’s Clinical Practice Research Datalink (CPRD) that chronicles some 20 million persons who received primary medical care over a period as long as thirty years. EHRs offer great potential in CER, however they have many issues that make analyses challenging and complicated. The data sets are large and getting larger; there is a significant amount of missing data; it is high dimensional and the dimensionality is growing rapidly; there are errors and outliers; some patients enter the database and then leave or leave and come back; patients enter the data base at varying times in their life and new patients are always arriving and others leave as they die or move away. Our aims are to develop analytical methods that address these key issues and facilitate rigorous comparison of the clinical effectiveness of candidate drugs with a reference therapy for selected medical disorders. The contributions we expect to make include development of analytical strategies that (1) more effectively handle missing data; and (2) deal with high dimensionality in a way that facilitates allowing the methods to determine the relative importance of the covariates, without relying exclusively on clinical judgment.

Prerequisites: Interest in medicine or health care and facility with large datasets

Contact: Stan Finkelstrein: snf@mit.edu


12/13/18

IAP/Spring

Multiple Openings

UROP Department, Lab or Center: Biology (Course 7)

MIT Faculty Supervisor Name: Ankur Jain

Project #1: Understanding RNA self-assembly

Project Description: In this project, we will study how DNA and RNA can assemble into liquids and gels in a sequence dependent manner. We will investigate how certain disease associated mutations such as the ones observed in amyotrophic lateral sclerosis lead to misfolding and aggregation of RNA. Students will learn DNA/RNA preparatory and structure analysis methods, microscopy sample preparation, data collection and analysis.

______________

Project #2: Rules governing cellular RNA localization

Project Description: In project 2, students will study how the sequence of a RNA determines its cellular localization. The students will use molecular cloning, cell culture and live-cell fluorescence microscopy, and obtain training a variety of image analysis methods.

Contact: Beverly Dobson: bmdobson@wi.mit.edu


12/13/18

IAP/Spring

UROP Department, Lab or Center: Earth, Atmospheric, and Planetary Sciences (Course 12)

MIT Faculty Supervisor Name: Michael Follows

Project Title: Development and deployment of ecological models in the cloud using GitHub, Jupyter, and Julia

Project Description: Our research is focused on understanding and interpreting the distribution and function of diverse microbial populations in the ocean (Darwin Project, CBIOMES). A central element of our work is to use novel numerical models of diverse marine ecosystems and carbon cycle. The goal of the UROP project will be to develop and deploy interactive simulations of surface ocean plankton communities and their self-organization. We will use the Julia language in the form of Jupyter notebooks, and the models will be designed for ecological research and education, including in the classroom at MIT. This project will provide an opportunity to employ and highlight your software development and programming skills, and to make an impact on education and outreach. You will also gain insight into marine ecology and carbon cycle.

Prerequisites: Some experience with, or interest in, GitHub, Jupyter notebooks, and Julia.

Relevant URL: http://darwinproject.mit.edu/, https://cbiomes.org/

Contact: Gael Forget: gforget@mit.edu


12/13/18

IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Antonio Torralba

Project Title: Unity Game Engine Development for VirtualHome

Project Description: We are going to find some students who are interested in Unity Video Game Engine development. The environment is based on our VirtualHome (http://virtual-home.org/). The purpose is to generate videos with multiple people.

We have created some indoor scenes, agents and human actions. You can find some examples in the website (http://virtual-home.org/). We want to implement controlling multiple agents in this environment, including human-objects interactions and human-human interactions, and generating videos. To do this, we need to predefine some atom actions and implement them by writing Unity scripts.

Prerequisites:

  • Experienced in Unity Video Game Engine;
  • Experienced in Python and C#;

Relevant URL: http://virtual-home.org/; https://unity3d.com

Contact: Shuang Li: lishuang@mit.edu


12/13/18

IAP/Spring/Summer

UROP Department, Lab or Center: Mechanical Engineering (Course 2)

MIT Faculty Supervisor Name: George Barbastathis

Project Title: Machine Learning for Computational Image Formation and Inverse Problems

Project Description: The Optics Lab has several UROP openings available for students interested in applying Machine Learning techniques to Computational Imaging, in particular image formation under adverse circumstances such as very low photon count, poor dynamic range; motion blur; wavefront aberrations including presence of scattering media in the optical path; etc. The general approach is to train a Deep Neural Network (DNN) using examples of known objects and their distorted images through the optical system, then test if the DNN is capable of compensating for the distortions when objects previously not shown to it are imaged. Applications include photography, microscopy, autonomous navigation, and x-ray medical imaging with lower exposure of patients to harmful radiation.

Prerequisites: 18.03, 2.004, 2.671, 2.71. If you are missing some of these prerequisites but are still keen on working in our lab, please discuss with the Professor.

Contact: George Barbastathis: gbarb@mit.edu


12/12/18

IAP

Multiple Openings

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Una-May O'Reilly

Project #1: Robust Deep Learning

Project Description: Recent works show that deep learning models are brittle to adversarial examples. How can we make our models robust against these adversaries? This project investigates max-margin deep-learning models and contributing to open-source adversarial learning tools.

Prerequisites: Preferred pre-reqs: Pytorch, understanding of basic ML models (e.g., SVM and feedforward networks).

_____________

Project #2: Abstract Syntax Trees-Based Deep Learning

Project Description: How is it possible to discriminate between malicious and regular shell scripts? Some deep learning attempts use a traditional natural language processing setup while others use a character level setup. While these representations may express salient script properties, our hypothesis is that representations from static program analysis, such as abstract syntax trees, will be more effective. This project aims to develop deep learning models using abstract syntax tree representations of programs.

Prerequisites: Preferred pre-reqs: Pytorch, understanding of basic ML models (e.g., feedforward and recurrent networks).

_____________

Project #3: Learner Behavior Patterns in Massive Open Online Courses

Project Description: How do students learn computational thinking and programming online? Is it possible to identify behavioral patterns of successful learning? This project offers a chance to work on the  data science of online learning.

_____________

Project #4: Robust Black-Box Optimization

Project Description: Many real-world applications involve an adversary and/or uncertainty, specifically in the security domain. How can we model these adversaries in black-box settings where gradients are neither symbolically nor numerically available, or they are complex to compute. Recently, a Bayesian-Optimization framework has been proposed to compute equilibria in multi-player black-box continuous games. This project investigates the framework under the lens of robust black-box optimization.

Prerequisites: Preferred pre-reqs: Python, Basic understanding of Gaussian Processes.

_____________

Project #5: Autonomous Cyber Hunting

Project Description: How is counter-attack a useful  form of cyber defense? How can a cyber defense be improved with hunting for adversaries in the network and on devices? This project involves investigating how to detect novel malware.

_____________

Relevant URL: alfagroup.csail.mit.edu

Contact: ALFA Group: alfa-apply@csail.mit.edu


12/12/18

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Fadel Adib

Project Title: Learning Food Quality and Safety using Smartphones

Project Description: Over the past decade, we have witnessed many safety hazards that could've been avoided if we had access to ubiquitous food quality and sensing technologies. Our group recently developed the first system that can use off-the-shelf wireless stickers to detect food contaminants. At a high level, our system leverages RFID (Radio Frequency Identification) stickers that are already attached to hundreds of billions objects to sense food contaminants without direct contact with the food. We have developed machine learning models that can classify and detect different types of adulterants and contaminants, and our system can already detect fake alcohol and adulterated baby formula.

Here is a link to recent coverage by TechCrunch describing our technology: https://techcrunch.com/2018/11/14/rfid-stickers-could-signal-contaminated-food/

The goal of this position is to develop a smartphone app to interface with and control the technology. The app could be used by lay consumers in order to detect food contaminants.

Responsibilities: Your responsibility in this project will be to develop a smartphone app (iOS or Android) that can interface with this technology. The app would need to communicate with our technology through a simple client-server architecture in order to control the device as well as display its classification result on the smartphone screen. You will also be regularly meeting with the research team working on the wireless technology and learning algorithms.

Note: If you are interested in getting involved with developing machine learning algorithms for the technology, we will be happy to involve you as well. However, machine learning background is not a requirement for this UROP position.

Prerequisites:

  • iOS or android development
  • client-server programming

Contact: Unsoo Ha: unsoo@mit.edu


12/12/18

IAP/Spring/Summer

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Yoel Fink

Project Title: Optical fibers with embedded III-V sphere resonators

Project Description: III-V compound semiconductors are widely used for photonic applications, yet they rarely appear in a shape of spheres with atomically smooth surfaces. Here we will mass-produce millions of such spheres within a silica fiber. The silica will be responsible for guiding the light to excite the III-V spherical resonators. The application of such fiber includes distributed detection and light emission, quantum optics, and solar cells. We are seeking UROPs to assist with the fiber drawing and characterization – electrically and optically. The UROPs can expect to learn about fiber fabrication, material studies, and optical characterization.

Prerequisites: The project is open to all students in relevant fields.

Contact: Shai Maayani: maayani@mit.edu


12/12/18

IAP/Spring/Summer

UROP Department, Lab or Center: Comparative Media Studies (21 CMS)

MIT Faculty Supervisor Name: Philip Tan

Project Title: CLEVR - Collaborative Learning Environments in VR

Project Description: The MIT Game Lab and Education Arcade are developing and pilot testing a proof-of-concept VR game for a high school audience. We want to help students understand relative scale in Biology and collaborate with each other on problem-solving. We currently have a 3D environment of the interior of a human cell, in which one person in VR is exploring, and a tablet-based companion app for non-VR players to interact with the in-VR player.

We are looking for 4 UROPs to join our development team this IAP through Summer, to develop and refine our VR and tablet gameplay.

Positions available:

  • Programmer x2
  • Game designer x2

Positions opening in June:

  • Artist (3D or 2D) x2
  • Scrummaster

UROP Responsibilities may include:

  • Working alongside and reporting to MIT Game Lab/Education Arcade staff developers
  • Development of our VR game in Unity, for the Oculus Rift and Microsoft Surface.
  • Designing, modeling, and animating 3D assets for use in-game of molecules, proteins, DNA, and organelles.
  • Collaborating with other UROPs to design, implement, test, and iterate on game features.

Prerequisites: relevant experience may include:

  • Unity and/or C#
  • Game design
  • Developing for VR headsets
  • Developing for touchscreens
  • 3D modeling and animation
  • 2D illustration and graphic design
  • User Interface design
  • Github
  • Working in teams

Relevant URL: https://education.mit.edu/project/clevr/

Contact: To apply to any of these UROPs, please send a resume, link to portfolio, and cover letter (stating which Project Title you are interested in) to Rik Eberhardt . We would like to conduct interviews ASAP for students starting in IAP (January 8).


12/12/18

IAP/Spring

UROP Department, Lab or Center: Materials Science and Engineering (Course 3)

MIT Faculty Supervisor Name: Jeffrey C. Grossman

Project Title: Development of Web-based framework for data management for the Consortium for Production of Affordable of Carbon Fibers

Project Description: The web-framework as currently implemented may use Node.js based technologies for the back-end with direct connection to the MongoDB database, and Angular for the front end. The web interface is designed to: (a) provide a virtual notebook to track device fabrication and performance, (b) provide a fully searchable platform for retrieving specific and aggregated device data. While the current framework is in place, the candidate will be responsible of the extension of the schema for data submitted, integration of machine learning tools with the data-management and data visualization.

The CPACF program is supported by the Department of Energy, focusing on development and scale-up of inexpensive and abundant chemical precursors as well as manufacturing methods for the large scale deployment of inexpensive carbon fibers for automotive, with resulting improvements in vehicle efficiency through significant weight reduction. The vertically integrated consortium involves academic, national laboratories and industrial members each involved in specific steps of the production of carbon fibers, from chemical precursors to composites. The Grossman Group in the Department of Materials Science and Engineering (DMSE, Course 3) directs and coordinates the data management, analytics and modeling for the program. Each member will use the MIT developed web-based data management framework for data collection.

Prerequisites: You will work with DMSE researchers to build, test, and fully document the front-end, backend and integration with mongoDB, based on data-structures under development through the program. Interested candidates should have extensive and documented experience in web-design and development using Node.js and web technologies as well as MongoDB. Proficient hands-on experience with Python is desired. No specific Chemistry, Physics, Materials knowledge is required.

Contact: Nicola Ferralis: ferralis@mit.edu


12/12/18

Term: IAP/Spring

UROP Department, Lab or Center: Research Lab for Electronics (RLE)

MIT Faculty Supervisor Name: Stefanie Shattuck-Hufnagel

Project Title: Acoustic analysis of speech cues

Project Description: UROP team project in speech analysis, IAP and Spring term Interested in the links among speech signal processing, linguistics and human speech recognition?  Have some background (or strong interest) in either phonetics/phonology, computer science or cognitive science?  In this UROP you will learn to label the individual cues to speech sounds in the speech signal by hand, to create a database for evaluating a prototype automatic speech analysis system that is in development. In the process you will learn to recognize the many ways in which a word or sound can vary in different contexts, and why this is important for understanding human speech processing, as well as for improving automatic speech recognition systems, and potentially designing interventions for speakers who are experiencing speech difficulties due to disease, injury or developmental delay. 

The position involves 20 hours per week of training during the 4 weeks of IAP, and then 10 hours per week during the spring term, applying the training to labelling the acoustic cue patterns in samples of speech from many different speakers and several different domains.  Possibility of advancing to coding modules for the developing speech analysis system, in the later stages of the project.  Pays $12 per hour.  Contact Dr. Stefanie Shattuck-Hufnagel, sshuf@mit.edu

Prerequisites: Suggested: interest in or background in phonetics/phonology, computer science or cognitive science

Contact: Stefanie Shattuck-Hufnagel (sshuf@mit.edu)


1211/18

IAP/Spring

UROP Department, Lab or Center: Mechanical Engineering (Course 2)

MIT Faculty Supervisor Name: John Heywood

Project Title: Biomass to Power Novel Engine Concept

Project Description: We are working on using an internal combustion engine in a novel way to clean-up the producer gas from biomass gasification from organic contaminants (tar). The concept, if successful, would result in significant cost reductions in using biomass to power for remote electrification in the developing world as it addresses the biggest issue with biomass gasification today. You 'd be working with a post-docs on running the engine with gasifier produced gas, making chemical measurements, controlling key parameters of operation. Mostly hands-on work. No prior experience necessary.

Contact: Dr. Yu Chen (yuc@mit.edu), Dr. Emmanuel Kasseris (kasseris@mit.edu)


1211/18

IAP/Spring

UROP Department, Lab or Center: Chemical Engineering (Course 10)

MIT Faculty Supervisor Name: Michael S. Strano

Project Title: Nanoscale biosensors for in vivo monitoring of drug delivery, treatments, and response for cancer, diabetes, and sepsis

Project Description: Prof. Strano’s laboratory focuses on the development of nanoscale biosensors and materials for a wide array of applications. A growing area of our research is the development of nano-biosensors for medical applications in particular in cancer, diabetes, bacterial sepsis, and hematology to probe biologically relevant analytes in vivo and in real-time. Recently, we have developed a series of near-infrared (nIR) fluorescent probes for sensing reactive oxygen species, reactive nitrogen species, chemotherapeutic drugs, insulin, and are looking to develop new sensors. Our lab has customized these nanosensors for a wide range of biomedical analytes such as saccharides, dopamine and neurotransmitters, glucose, insulin, and cortisol. Our lab is pursuing a number of applications in cancer research to study chemotherapeutic drug delivery, tumor development, progression, and response to therapies. This includes identification of potential cancer biomarkers, in vitro validation studies (cell cultures and 3D models), and in vivo studies in orthotopic xenograft animal models. This new nanoscale sensor-imaging platform will allow for monitoring biomolecular changes at earlier time points following chemotherapy, radiation therapy, or immunotherapies, clinicians will be able to more promptly adapt the patient’s treatment strategy depending on the tumor response. For the diabetes, we are interested in continuing to develop our insulin sensors to determine and tune its sensitivity and specificity as we transition the validation of the sensor into in vitro and in vivo. For bacterial sepsis, we are interested in developing new nanoscale biosensors for studying drug delivery and drug susceptibilities. For hematology, we are interested in developing new nanoscale biosensors for serologic tests. We also work on the development of in vitro 3D tumor tissue models and the development of hydrogel (polymer chemistry) constructs to study our sensors in vitro initially before transitioning to in vivo. We are also working on computational models for studying the delivery and diffusion of both drugs into human tissue for cancer and diabetes.

As a student on this project, you will be exposed to a very diverse and interdisciplinary research project and lab. You will have the opportunity to learn many different areas of research ranging from synthesis and characterization of our bionanosensors, to development and testing of biocompatible form-factors (hydrogels) to encapsulate our bionanosensors, testing the sensitivity and specificity of the sensors to the analyte of interest, to testing of sensor response in solution phase, in cell cultures or 3D tumor models, and ultimately in xenograft orthotopic cancer animal models. Students will also have the opportunity to learn several optical techniques such as fluorescence spectroscopy, absorption spectroscopy, and Raman spectroscopy. Students may also choose to be involved in the design and development of optical systems (free space and fiber optic) including optical design, hardware instrumentation, and software instrumentation.

Student's specific project will be tailored to the student's particular interest, research needs of our lab, and student's previous experience.

Prerequisites: Students interested in year long or longer research opportunity with interest in biology, chemistry, bioengineering, materials science, mechanical engineering, chemical engineering, physics, or optics. Multiple student positions are available for these projects.

Relevant URLs: srg.mit.edu freddynguyen.org

Contact: Freddy Nguyen, MD, PhD: freddytn@mit.edu


12/11/18

IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Poggio

Project Title: Deep learning

Project Description: The Poggio lab is looking for motivated students, with experience in deep learning and interest in brain sciences. The student will help in the experiments of a paper submission.

Prerequisites:

  • Expertise in deep learning
  • Full-time availability during IAP

Contact: If you are interested, send CV detailing experience in deep learning to Xavier Boix xboix@mit.edu


12/11/18

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: V. Michael Bove

Project Title: Large User Interface using Gesture and Voice

Project Description: Given the rise of augmented reality and new 4k/8k displays, we are creating a new gesture and/or voice-controlled Large User Interface (LUI) that allows a user to manipulate visual information on very large screens. The gestures and voice input will be mapped to ReactJS web elements to provide a highly-responsive and accessible user experience. The menu screen will of an extendable list of applications, currently including photos, YouTube, etc, which are navigated through our input framework.

Importance should be placed on UI/UX and visuals. Examples can be leveraging UI/UX animation skills on top of the ReactJS platform to make the content more interactive and engaging. Interested in leveraging libraries such as particles.js or animate.css, The user will work with Vik to create such applications and deploy them to the interface on a large 4k/8k display.

Prerequisites:

  • UI/UX design for web (required)
  • ReactJS Web Development (preferred)
  • Leap Motion experience (optional)
  • Google Home experience (optional)

Relevant URL: https://www.media.mit.edu/projects/large-user-interface-with-gesture-and-voice-feedback/overview/

Contact: Vik Parthiban: vparth@mit.edu


12/7/18

IAP/Spring

UROP Department, Lab or Center: Brain and Cognitive Sciences (Course 9)

MIT Faculty Supervisor Name: Pawan Sinha

Project Title: Game design to characterize dynamic object interactions in autism spectrum disorders

Project Description: Difficulty interacting with dynamic objects represents a salient, yet understudied, feature of the autism phenotype. These difficulties can present grave consequences for individuals with autism. Motivated by a theory we have proposed (Sinha et al., 2014), the overarching hypothesis guiding this project is that difficulties in interacting with moving objects may be an outcome of impaired prediction of the trajectory of moving objects as they unfold over time. In collaboration with the ActionLab at Northeastern University, this project aims to investigate these abilities across a range of motor tasks to test whether the difficulties seen clinically result from an underlying impairment in temporal prediction. The project uses gamified motor tasks to collect quantitative data regarding movement of individuals relative to objects in the environment. Deeper knowledge of these abilities holds relevance for adapting environments for children and adults with ASD, as well as for designing interventions that acknowledge and address potential underlying neurocognitive issues (e.g., prediction), and not merely the manifestation of the underlying impairment (e.g., difficulty in catching a ball).

Position Description: The primary responsibility of this UROP will be to contribute to the design of web-based games that engage school-age children. The student will work closely with others as part of a team, with substantial opportunity to work independently. This position is available for pay or credit.

Prerequisites:

  • The ideal candidate will have previous experience with digital game design, graphic design (e.g. Photoshop, Illustrator), and/or computer animation
  • Able to commit at least 20 hours per week during IAP and 8-10 hours per week during the semester.
  • Experience in graphic design, game interface design/testing, user interfaces, or related fields.
  • Ability to travel occasionally to Northeastern University for training or meetings desirable, but not required. (Northeastern is a 30 minute walk from MIT, and is also accessible via shuttle/public transport.)

Application and Deadlines: There is no application deadline for those who wish to volunteer. For direct funding* or credit, please refer to the UROP office deadlines. We recommend that you contact us at the start of the semester if you are interested, or at least several weeks in advance of the UROP deadline for which you wish to apply. We will begin reviewing applications on a rolling basis until the position is filled. Visit the UROP website for details about UROP requirements: http://web.mit.edu/urop/apply/deadlines.html

Contact: If you are interested in joining our team, please e-mail Annie Cardinaux, Project Coordinator at anniec@mit.edu, and include your Resume/CV/Portfolio and a letter describing your interest in and qualifications for the project. In your e-mail, please specify whether you would like to do the UROP for pay or credit.

*If you are selected and wish to complete the UROP for pay, you will need to create a project proposal for review by a member of our research team several days in advance of the relevant UROP deadline for that particular semester.


12/7/18

IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Aude Oliva

Project Title: Crowdsourcing attention and memory with web and mobile

Project Description: The Computational Perception and Cognition Lab (http://cvcl.mit.edu/aude.htm) is looking for motivated undergrads with web dev experience to join the team and help develop interactive user interfaces for web and mobile, to record people's attention and memory performance on videos and graphic designs through a series of crowdsourcing perception/cognition experiments and games. Looking for students for IAP, Spring, and Summer. If interested, please send examples of past UI/web projects, GitHub repos, and CVs to: zoya@mit.edu.

Prerequisites: Required: proficiency with python, javascript, html, jquery.

Recommended: 6.148, 6.170 or similar.

Bonus: 6.036, 6.819, or similar.

Relevant URLs: http://cvcl.mit.edu/publications.html

Contact: Zoya Bylinskii: zoya@mit.edu


12/7/18

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Hiroshi Ishii

Project Title: Synthesis of novel biodegradable photopolymers for 3D printing

Project Description: Conventional stereolithography 3D printing uses acrylic-based monomers for polymerization. The presence of oxygen in the air prevents full curing on the surface of the print. Therefore, a post-curing process is always needed. This research deals with the synthesis and exploration of alkyne carbonate derivatives as biodegradable building blocks in the Thiol–ene photopolymerisation reaction to facilitate the 3D printing of dense microstructures. The Thiol-ene reaction offers higher rate of polymerization therefore eliminates the post-curing process

Your task will be synthesizing the photopolymer under instruction; reading literature research and iterate the design of the photopolymer, and characterize the photopolymer’s mechanical properties. The developed material will be used for a 3D printing fashion design, which will be featured in the Cooper Hewitt Museum in New York City in 2019.

Prerequisites: Experience in chemistry

Contact: Jifei Ou: jifei@mit.edu


12/7/18

IAP/Spring

UROP Department, Sloan School of Management (Course 15)

MIT Faculty Supervisor Name: Georgia Perakis

Project Title: Big Data and Online Platforms Analytics – Work with a Large Retailer in Latin America

Project Description: How competition and pricing strategies affect consumer behavior in online platforms? This project will combine two pieces: (1) Analyzing big datasets to model consumer behavior in competitive markets. (2) Running field experiments. We will use tools from machine learning, analytics, A/B testing, experimentation. This is a great opportunity to work in a fun and challenging research environment.

Responsibilities: (1) You will be a full member of the research team, and the team will meet weekly. (2) In the meetings we work on modelling, algorithms, discussing results, and setting up next steps. (3) The main responsibilities will be pre-processing data, coding algorithms (with our guidance), and analyzing data.

Qualifications:

  1. Some experience with Python and/or R and/or Stata is required
  2. Basic knowledge of Spanish is not necessary, but a plus.
  3. Available to start during IAP and Spring semester

Contact: Please send applications to Professor Georgia Perakis (georgiap@mit.edu), Diego Aparicio PhD student (dapa@mit.edu), Tamar Cohen PhD student (tcohen@mit.edu). Please email resume + a 2-sentence statement of why you are interested and a good fit for this project.


12/5/18

IAP/Spring

UROP Department, Lab or Center: Urban Studies and Planning (Course 11)

MIT Faculty Supervisor Name: Ezra Haber Glenn

Project Title: "Urban Planning in the News" case-bank/sourcebook

Project Description: The project seeks an undergraduate student with an interest in urban history, public policy, media studies, and/or journalism to help assemble an online case-bank and source-book of news stories for use in the "Urban Planning in the News" seminar. and related outreach.

Student(s) will research, collect, summarize, and organize news coverage (local and national; print and digital; news and commentary; left and right; contemporary and historical; etc.) tracing specific projects or controversies to illustrate and explore topics in urban planning, such as climate change adaptation; affordability and segregation; economic development and downtown revitalization; development of mass transit and transportation networks; race and policing; and other contemporary and historical debates.

For pay or academic credit.

Prerequisites: Strong writing and organizational skills; experience with online and library archival research, especially newspaper/magazines; interest in/familiarity with urban history and/or contemporary urban planning and policy debates; ability to be self-directed.

Contact: Ezra Haber Glenn: eglenn@mit.edu


12/5/18

IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Mike Bove

Project Title: Looking Sideways creative online exploration tool

Project Description: During IAP and Spring semester in the Media Lab’s Object-Based Media group, we’ll be continuing to develop computational design tools that embrace ambiguity and serendipity, vital for technologies used early in the creative process.  We’ll be refining the Sideways Search inspiration exploration tool (http://pathways.surge.sh/conceptnet/full/), an online browsing tool that seeks to provoke unexpected inspiration and guide pathways to new ideas through providing users with a selection of semi-randomly chosen, loosely related, diverse sources from art, design, history and literature for every search query.  We’ll be carrying out user studies and integrating that feedback into modifications of the tools, e.g. refining the UI and building new features.  There is also opportunity to work on the design(human)design tool (http://designhumandesign.media.mit.edu/), and help integrate these tools into a projection mapping table so that they can be viewed in a more immersive environment.

Skills required:

  • JavaScript (ideally with knowledge of D3 visualization library)
  • Front end web development

Contact: Philippa Mothersill: pip@mit.edu


12/5/18

IAP/Spring

UROP Department, Lab or Center: The MIT Energy Initiative (MITEI)

MIT Faculty Supervisor Name: Francis O'Sullivan

Project Title: Techno-economics of energy storage systems

Project Description: Does the topic of energy storage fascinate you? Have you ever wondered about some of the business cases for advanced energy storage systems, especially in the context of a low-carbon energy future? If yes, we have some exciting ongoing projects that you could work on.

Project #1: Investigation of the techno-economics of hybrid systems using gas-turbines and advanced energy storage systems

We’re investigating the techno-economics of hybrid systems that employ gas-turbines together with advanced energy storage devices such as batteries. As renewables become a more prevalent source our energy in our society, our electrical grid will need to adapt accordingly to account for the variable nature of resources such as wind and solar PV. As the system transitions, conventional generators such as gas turbines often have to ramp up and down rapidly to address this variability and to make sure that the load is met. Such rapid ramps come at a cost to these systems in the form of efficiency losses and higher operational and maintenance costs. Can energy storage systems such as batteries, thermal storage or other chemical storage options play a role here by alleviating the need for these ramps and allowing the gas turbine to operate at more economical levels or are these storage options too expensive to provide such a service? Also, while storage may improve gas turbine operation, how does this integration affect the system as a whole? We’re using operational data from gas turbines to answer questions of these sort, which have significant real-world implications as we devise ways to address climate change more efficiently. We invite you to come be a part of our team.

Requirements:

  • Programming:
    • Fluency with Python
    • Modeling experience (preferred not required)
    • familiarity with processing modeling platforms like Aspen Plus or HYSYS (preferred not required)
  • Web Development (preferred not required):
    • Experience with Django
    • Fluency in PhP
    • Experience in web programming and database development

____________

Project #2: Techno-economic analysis of the costs of advanced energy storage systems: Energy Storage Technology Assessment Tool (ESTAT) Development

The project is on the development of a web-based energy storage technology cost/benefit assessment tool. Our research will examine the techno-economic tradeoffs between existing and emerging energy storage technologies. The initial framework as well as an online version of the tool has already been developed. The selected candidate will work on further developing the tool. In particular, and during the UROP period, we’ll seek to investigate hydrogen-based systems and flow batteries. This is a great opportunity to learn about advanced energy storage technologies and how to evaluate their costs and benefits. We are looking for someone with a background in chemical engineering, programming and ideally have web development experience. Detailed requirements listed below.

Requirements:

  • Some fundamental understanding of electrochemical energy storage systems
  • Programming:
    • Fluency with Python
    • Modeling experience (preferred not required)
  • Web Development (preferred not required):
    • Experience with Django
    • Fluency in PhP
    • Experience in web programming and database development

____________

Project #3: Development of a co-optimization model for energy storage with offshore wind

We are in the process of developing a decision-making framework that investigates the costs and benefits of advanced energy storage systems paired with offshore wind farms. The selected candidate will participate in advancing the development of the existing model. This is a great opportunity to learn about energy storage technologies and how to evaluate their costs and benefits, especially in the context of offshore wind farms. The experience gained can be put to use in similar decision-making frameworks and will be a useful skill to have. Detailed requirements listed below.

Requirements:

  • Familiarity with optimization techniques
  • Fluency/familiarity with Julia programming language
  • Some familiarity with electricity markets and services (preferred not required)
  • Some familiarity with energy storage systems such as batteries (preferred not required)

Contact: Apurba Sakti: sakti@mit.edu


12/5/18

IAP/Spring

UROP Department, Lab or Center: Mechanical Engineering (Course 2)

MIT Faculty Supervisor Name: Douglas Hart

Project Title: Prototyping suction-based mechanism for underwater ROV attachment and locomotion

Project Description: Underwater remotely operated vehicles or ROVs for short, are used in many applications ranging from deep sea exploration to specific tasks such as inspection and cleaning of surfaces underwater. These surfaces could be on stationary structures such as tanks, pipes or oil rigs or moving structures such as ships, submarines, or even marine animals. ROVs typically use thrusters to maneuver underwater. For applications in high ocean currents or where a close distance is required between the ROV and the surface of interest, thrusters may not be the optimal solution. This research aims at designing, prototyping, testing, and validating a suction-based mechanism for ROVs so that they can attach to surfaces underwater and traverse them.

As a UROP you will get to be part of every element of the design process in which getting your hands dirty is crucial. If you are interested please reach out to me (acobi@mit.edu) asap so we can schedule a time to meet and discuss. Ideally the for-credit UROP would start immediately or at the beginning of IAP.

Prerequisites:

  • Machine Shop skills (lathe/mill/waterjet)
  • Rapid Prototyping skills (laser cutter/3D printer/hand tools)
  • Programming is a plus

Contact: Alban Cobi: acobi@mit.edu


12/3/18

Term: IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Steven Barrett (AeroAstro Department)

Project Title: High energy density battery prototyping for a solid-state ion airplane

Project Description: An interdisciplinary team from AeroAstro and EECS flew the first solid-state ion airplane, recently published in Nature (https://www.nature.com/articles/s41586-018-0707-9). Work is currently underway to develop a 2nd-generation aircraft which will be able to fly faster and for longer. High energy density energy storage is crucial for this new aircraft. We are seeking a UROP to undertake research of currently available battery technology and develop the battery pack to be used on the aircraft.

Prerequisites:

  • The student is required to have taken 6.002.
  • Some experience in electronics prototyping and battery systems would be very helpful.
  • The student is required to commit at least 10 hours a week.

Relevant URL: http://electricaircraft.mit.edu/, http://lae.mit.edu

Contact: Haofeng Xu (xuh@mit.edu)


12/3/18

Term: IAP

UROP Department, Lab or Center: Urban Studies and Planning (Course 11)

MIT Faculty Supervisor Name: Justin Steil

Project Title: Analyzing Eviction Dynamics

Project Description: Housing advocates and city officials are increasingly describing the eviction rate in Boston as a crisis.  Legal services lawyers and community organizers report intense displacement pressures in low-income, neighborhoods and in communities of color as these neighborhoods gentrify and rents rise.  Policy analysts and researchers also point out that as home values rise, higher income populations are remaining renters longer, resulting in a tightening of the rental market and an increase in rental prices.  Evictions appear to be widespread in low-income communities amidst these market changes.  Yet while there is near consensus that the rate of evictions is alarming, there is still much we do not know about the precise extent to which evictions are occurring, where they are occurring, and which individuals and/or communities are most vulnerable.

The goal of this project is to better understand the dynamics of eviction in Boston in order to more effectively design policy solutions and more appropriately target legal representation.  Our specific goals fall into two overarching categories: identifying the prevalence of displacement caused by eviction filings, by reviewing case files to identify cases in which tenants sign agreements to move out of their home, and; mapping who is most vulnerable to eviction, by reviewing case files to identify characteristics of the eviction filing, the legal procedure the case followed, of the property, and of the property owner to better understand who is able to remain in their unit after an eviction is filed and who ends up being formally evicted or agreeing to move out.

To gather initial data for this research we will first scrape available data from the MassCourts website, to obtain a count of eviction filings, party names, property addresses, etc.  We will then merge the eviction filing data with parcel level data about building types and property ownership from the City of Boston Assessing Department.

To obtain the data about the dynamics of each eviction, we will randomly select a total of 1,500 files spread across the years 2013 to 2018 and request those files from the Massachusetts Housing Court.  We will photograph each page of each of those files and then code each file systematically to identify the relevant details of each case, including: the type of eviction filing, the amount owed and time in arrears for non-payment filings, the outcome (default judgement, agreement for judgment, trial), the substance of any agreement for judgment (time to pay, etc), whether the defendant was represented by counsel, whether an answer was filed and discovery requested, whether affirmative defenses and counterclaims were asserted, and whether a jury trial was demanded.

Once the files are coded, we will merge them with the MassCourts data and the Assessing data to create a robust dataset regarding eviction dynamics. With those data, we will first be able to estimate the effective eviction rate by identifying the rough share of eviction filings that end in a tenant’s departure from the unit.  We will also be able to estimate time trends over the past five years and differences by neighborhood in completed evictions and move outs.  Second, we will conduct logistic regressions to estimate the relationship between completed evictions and move out agreements, on the one hand, and characteristics of the building, the landlord, the eviction filing and the procedural history to better understand who is most vulnerable to displacement as a result of an eviction filing.  It will also allow us to estimate what factors may protect against displacement and allow residents to remain in their homes.

We are looking for UROPS to work during IAP to do 4 things:

  1. To go to court, obtain eviction files, and photograph those files;
  2. To code those files, which we will explain how to do;
  3. If you have programming skills, to scrape the data from MassCourts and from the Assessing office;
  4. To partner with us in analyzing the data, if you are interested.

We hope to complete the data gathering and coding during IAP and will work closely with the students on both aspects of the work.  Ideally we would start on Monday, January 7, but the work itself can happen on a somewhat flexible schedule.

Prerequisites: None.

Contact: Justin Steil (steil@mit.edu)


11/30/18

Term: Spring

UROP Department, Lab or Center: Civil and Environmental Engineering (Course 1)

MIT Faculty Supervisor Name: Prof. John Williams

Project Title: Cryptocurrency Mining: a sustainable approach

Project Description: A major criticism of Bitcoin and other cryptocurrencies is their massive waste of energy used in performing the mining that secures the transactions. This mining involves solving a simple but statistically difficult problem. A better alternative would be to use this processing might for more sustainable uses. This project will involve researching various ways of using the might of mining computation in a way that benefits humanity, and powers this new paradigm in the internet's story.

Prerequisites: General blockchain knowledge, experience in JavaScript/Node.js desired.

Contact: Sam Raymond (sjr@mit.edu)


11/30/18

Term: Spring

UROP Department, Lab or Center: Civil and Environmental Engineering (Course 1)

MIT Faculty Supervisor Name: Prof. John Williams

Project Title: New Applications of Ai in Numerical Simulation: Matrix Inversion

Project Description: Deep learning and other machine learning techniques have begun making real world impacts on everyday life, from translation in real-time to facial recognition. Much of the interest in neural networks lies in the convolutional neural layers. These layers allow the network to "read" an image. Currently, researchers are looking into alternative use cases for these layers. This project will focus on using these layers on matrix inversion. Matrix inversion is a crucial component to thousands of technologies used every day. This process has been studied by mathematicians for decades and the algorithms have become extremely elegant and complex to reduce the time taken to perform these inversions.

Prerequisites: Experience in MATLAB and/or Python desired.

Contact: Sam Raymond (sjr@mit.edu)


11/30/18

Term: Spring

UROP Department, Lab or Center: Civil and Environmental Engineering (Course 1)

MIT Faculty Supervisor Name: Prof. John Williams

Project Title: Fast Physics: Combining AI and Numerical Simulations for faster mechanics

Project Description: Deep learning and other machine learning techniques have begun making real world impacts on everyday life, from personal assistants to driverless cars. A new application of this area of Artificial Intelligence is in predicting the physics of a system as time unfolds. This project will focus on building a framework that uses synthetic data to train a neural network engine capable of understanding the laws of physics to speed up numerical simulations.

Prerequisites: Experience in TensorFlow and/or MATLABs Neural Network Framework desired.

Contact: Sam Raymond (sjr@mit.edu)


11/30/18

Term: Spring

UROP Department, Lab or Center: Civil and Environmental Engineering (Course 1)

MIT Faculty Supervisor Name: Prof. John Williams

Project Title: Additive Manufacturing – a simulation approach to understanding this complex process

Project Description: Creating new structures using the advances in additive manufacturing, or 3D printing, is allowing designers and engineers to build objects that were once thought impossible. A key tool of any designer is a simulator that can predict the critical stresses present in the structure under loading so that the safety can be ascertained. This project will look into further developing a 3D simulation engine to incorporate 3D printing processes to better understand the physics that occurs in the printing process.

Prerequisites: Experience in Python and/or C++ desired.

Contact: Sam Raymond (sjr@mit.edu)


11/30/18

Term: Spring

UROP Department, Lab or Center: Civil and Environmental Engineering (Course 1)

MIT Faculty Supervisor Name: Prof. John Williams

Project Title: Movie SFX simulations based on a real-world physics engine

Project Description: 3D simulations of real-world physics are becoming more and more common in modern triple A titles and blockbuster films. Creating these simulation engines means solving the complex equations of physics that the systems obey. Once a simulator has been created it needs to be tested in different physical use cases so that its results can be validated and the simulator's limits pushed. This project involves using a simulation engine developed at MIT to recreate various physical problems, such as tidal waves and asteroid impacts, so that the simulator can be pushed to its numerical limits.

Prerequisites: Experience in Python and/or C++ is desired.

Contact: Sam Raymond (sjr@mit.edu)


11/28/18

Term: IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Prof. Tayo Akinwande

Project Title: Modelling and Characterization of Silicon Nanowires and Silicon Field Emitter Arrays

Project Description: In this work, we are developing low voltage, high current density and reliable nanostructures based on Si field emitter arrays primarily for nano-vacuum channel transistors and nano-focused X-ray sources. To achieve this, we are fabricating high aspect ratio nanowires with integrated nano-tips, and incorporating an additional gate for electron beam focusing. We are also working on the fabrication and characterization of electron transparent structures such as graphene above the gate to potentially improve the lifetime in poor vacuum environments.

In this project, we have opportunities for several students to carry out the following tasks:

  1. Modelling and/or characterization of the thermal effects of Si nanowires and emission properties of large area density Si FEAs with Silvaco;
  2. Modelling electron transmission through a graphene membrane using COMSOL Multiphysics;
  3. Characterization and analysis of fabricated FEA tips in an ultra-high vacuum system; and
  4. Assistance in designing and assembly of a compact system for nano-focused X-ray emission.

Through this research project, the students will be able to gain insights on device physics and electrical characterization techniques for various applications using Si FEAs.

Contact: Girish Rughoobur (grughoob@mit.edu)


11/28/18

Term: IAP/Spring

UROP Department, Lab or Center: Comparative Media Studies (21 CMS)

MIT Faculty Supervisor Name: Eric Klopfer

Project Title: Mobile Educational games

Project Description:  Interested in games?  Want to work in web and app development? Apply to work on Participatory Simulations in STEP/TEA as a UROP!

Over many years and many iterations of technologies (from Palm Pilots to iPhones) we (Scheller Teacher Education Program and The Education Arcade) have been working on whole class systems-based mobile simulation games. Imagine a classroom game in which you must interact with others but avoid getting contaminated with a virus being passed around.  OR trying to maintain the balance in a classroom digital ecosystem while keeping yourself alive. These are just some of the scenarios we’ve developed over the years with a lot of success.

The latest iterations of these games are being designed for mobile devices including smartphones and tablets using portable technologies (React).  We are looking for UROPs to help design new iterations of these games and implement them using scalable web technologies.

The system is currently using React (js) and Firebase.  This IAP we plan to add a React Native client and, design and implement at least one new game.

Prerequisites: Requirements: We are looking for students with a strong programming background.  Experience with JavaScript/HTML/CSS, React/Redux/React Native and NoSQL databases is helpful.  Interests/expertise in front end design and game design are also big strengths. Availability to work during IAP for at least part-time at the STEP/TEA Lab is required.

If you are interested in this position, please send an email to tea-jobs@mit.edu and include:

  • An overview of your programming experience (specific references to relevant courses and other development and programming projects would be very helpful) including any pertinent URLs
  • A summary of any previous UROP and work experience (attach a resume if you have one)
  • A short description of why you are interested in working on this project
  • Please put “PSims" in the subject line

Contact: Eric Klopfer (tea-jobs@mit.edu)


11/21/18

Term: IAP/Spring

UROP Department, Lab or Center: Aeronautics and Astronautics (Course 16)

MIT Faculty Supervisor Name: Edward Crawley

Project Title: Interactive visualization for an autonomous resource allocation tool for communication satellites

Project Description: The System Architecture Lab in AeroAstro is looking for two UROPs to develop the front-end visualization for a dynamic resource management tool for communication satellites. The UROPs will be part of an exciting sponsored research project with SES, the largest satellite operator worldwide. The project aims to develop an autonomous dynamic resource allocation tool for their new fleet of flexible high-throughput satellites. 

The project is now in the state where we have a good understanding and description of the problem. We have done some initial modelling and developed some AI algorithms. However, due to the high complexity of the problem, we need help with an interactive visualization of the results. The front-end will also be used to communicate with the sponsor, so the UROPs will be key team members. There will be a clearly defined work package for each UROP.

Prerequisites:

  • Excited to work on a real and impactful project in close collaboration with industry
  • Creativity and out of the box thinking
  • Strong experience in Python
  • Experience with web-based development
  • Experience with Dash/plotly is a plus (we have started to create a first visualization with Dash)
  • Experience with CesiumJS or STK is a plus
  • Knowledge in RF communication is a plus

Since we also need help with setting up some infrastructure, we want to have one of the two UROPs focus more on the infrastructure, whereas the other one focuses on the visualization development. Please let us know about your preference in the application.

Because we want to make you integral part of the project, we are looking for longer term commitments with at least one day per week during Fall and Spring. Full-time availability during IAP is a plus. If you are interested in joining our team, contact us with your CV. We are looking forward hearing from you!

Contact: Markus Guerster (guerster@mit.edu)


11/21/18

Term: IAP/Spring

UROP Department, Lab or Center: Mechanical Engineering (Course 2)

MIT Faculty Supervisor Name: Prof. Ahmed Ghoniem

Project Title: Automation and control system for a decentralized biomass/renewable energy system

Project Description: In many parts of the developing world, agricultural and other biomass waste is simply burned in the open air, creating much toxic pollution. Thermochemical treatment is a process whereby this waste can be converted into solid fuel. This has the potential to provide renewable energy, create new income opportunities, reduce waste, and reduce pollution and greenhouse emissions.

The reactor has been set-up and operated successfully. However, the measurement of different parameters (weight, temperature, flowrate, pressure, level, etc.) is done manually at present. We need to add some automation and control to make the system more effective and operational with minimal user intervention. The UROP will contribute mainly in setting up data acquisition system, temperature feedback control, and motor controller for adjusting flowrate.

Prior experience with electronic circuit design, controllers/relay and Arduino platform is required. Multi-semester engagement would be preferred. Please send CV to sonalt@mit.edu in case of interest. Alternatively, if someone wish to participate in the laboratory experiments on biomass torrefaction at BATES laboratory (30 min drive from MIT, we will provide transportation), you are welcome to apply.

Prerequisites: Prior experience with electronic circuit design, controllers/relay and Arduino platform is required.

Relevant URL: http://tatacenter.mit.edu/portfolio/torrefaction-reactor/

Contact: Sonal Thengane (sonalt@mit.edu)


11/20/18

Term: IAP/Spring

UROP Department, Lab or Center: Political Science (Course 17)

MIT Faculty Supervisor Name: Charles Stewart III

Project Title: Data Initiatives in Election Science

Project Description: The need for science and transparency in the analysis of elections is an increasingly important part of our democracy. As the MIT Election and Data Science Lab (MEDSL) continues to grow we are are working towards the goal of becoming the foremost clearinghouse for election science data. Although much data exists, it is often hard to access and out of date. This UROP is an opportunity to be part of improving access to data and contributing to election science research.

As a UROP working on this project you will:

  • Collect and organize election science data related to the recent general election
  • Learn and use Git and GitHub for version control
  • Present interesting research ideas and findings to the larger group
  • Learn additional data management and statistical programming skills
  • Contribute to ongoing MEDSL research projects
  • Work towards answering your own research questions related to election science
  • Participate in lab meetings

Prerequisites: Some experience with statistics and data handling is required. You should also be familiar with data management in formats such as comma/text delimited and/or Excel-type spreadsheets. Additional programming or statistical software skills (e.g., Stata, R, Python) are desirable but not required. Please indicate your level of familiarity with data collection and management, web scraping, and statistical software packages when you apply. We welcome applications from students at both MIT and Wellesley. The UROP can be for credit or pay.

Relevant URL: electionlab.mit.edu

Contact: Dr. Cameron Wimpy (wimpy@mit.edu)


11/19/18

Term: Summer

UROP Department, Lab or Center: Mathematics (Course 18)

MIT Faculty Supervisor Name: Prof. Alan Edelman

Project Title: Mathematical Foundations of Big Data & Machine Learning

Project Description: Big Data describes a new era in the digital age where the volume, velocity, and variety of data created across a wide range of fields (e.g., internet search, healthcare, finance, social media, defense, ...) is increasing at a rate well beyond our ability to analyze the data. Machine Learning has emerged as a powerful tool for transforming this data into usable information. Many technologies (e.g., spreadsheets, databases, graphs, linear algebra, deep neural networks, ...) have been developed to address these challenges. The common theme amongst these technologies is the need to store and operate on data as whole collections instead of as individual data elements. This research explore the common mathematical foundation of these data collections that apply across a wide range of applications and technologies. Mathematics unify and simplify Big Data and Machine Learning. Understanding these mathematical foundations allows the user to see past the differences that lie on the surface of Big Data and Machine Learning applications and technologies and leverage their core mathematical similarities to solve the hardest Big Data and Machine Learning challenges. This projects seeks to strengthen the mathematical foundations of Big Data and Machine Learning.

Prerequisites: Matrix mathematics and/or linear algebra are helpful. Experience with Matlab, Octave, or Julia is helpful, but not a requirement.

Relevant URL: https://mitpress.mit.edu/books/mathematics-big-data

Contact: Dr. Jeremy Kepner (kepner@ll.mit.edu)


11/15/18

Term: IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Cynthia Breazeal

Project Title: A robot that can recognize and react to human emotions in real-time interaction

Project Description: Learning language and literacy at a young age is important, as children’s early language ability can impact their later educational success. A variety of technological interventions, such as social robotics, were designed to help support children’s early language learning (such as vocabulary skills). Thus, our project is to develop a robot peer that can facilitate children's English word learning in a personalized way.

To engage a child in a personalized word learning activity, the robot needs to assess the child's learning state (such as learning progress and semantic acquisition process) and adapts its interaction strategies to promote their learning. In the interaction, the robot should coach the child, providing hints and explanations when detecting the child's struggling with understanding a word's meaning. Conversely, the robot should also encourage the child to practice more by purposefully behaving as a tutee and letting the child to teach the robot. In our last project, we developed a Reinforcement Learning based robot behavior policy to enable a robot to deliver a personalized teaching strategy to a child.

The current robot behavior model does not recognize and react to any child’s real-time emotions (affects) expressed through their facial expressions, but we know that the emotional intelligence is crucial for robots to interact with children. Thus, we aim to develop an affective expression model that enables a robot to detect and analyze a child’s affects in real time during interaction and then select a personalized affective expression to the child.

You will integrate a cutting-edge affect recognition tool (Affdex) into our child-robot interaction system, implement robot’s behaviors, program two social robots (Jibo and Tega), and build a data analysis tool that will help uncover the interaction dynamics between a robot and a child.

Prerequisites: This position requires you to work at least 30 hours per week for the entire IAP period. You may continue the work in the spring semester. Please indicate whether you plan to do research for credits or fundings during IAP.

Required skills:

Python, APIs, statistical analysis, Unix

Relevant URL: http://robotic.media.mit.edu/

Contact: Huili Chen (hchen25@mit.edu)


11/15/18

Term: IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Cynthia Breazeal

Project Title: Develop a multimodal data synchronization and visualization platform for parent-child interaction data

Project Description: We are researchers from the Personal Robots Group at Media Lab! Really would love to have you in our group!

Context

Learning a language at a young age is very crucial for children’s later educational. However, a major problem faced by many children from low socioeconomic status (SES) families today in learning a language is a lack of resources and developmentally important stimulation in their homes and schools. This lack of relevant resources and experiences for many young low SES children leads to a detrimental effect on their language and literacy development. For example, preschool-age low-SES children raised in families had significantly smaller vocabularies than high-SES children, and these differences even magnified over time. Children from low-SES households appear to have less high-quality social, responsive and facilitative interactions with their parents, which are especially crucial for their language development.

Project Description

Thus, we are hoping to bridge this participation gap in early childhood learning. This project aims to understand the mechanisms behind parent-child language-related interaction among families with varying SES levels. We will be collecting a very rich dataset on such interaction between parent and child from multiple data modalities (e.g., vision, physiology, speech, brain activity). Since data will be collected on both a parent and child in real time for multiple families, it is very crucial to store, clean and synchronize data from multiple modalities and multiple interaction sessions seamlessly.

Your Responsibility

You will be designing and developing a multimodal data storage and synchronization tool that can automatically data from different modalities and across two stakeholders (a parent and a child) together. This tool will consist of two components: data synchronization and visualization. For the visualization part, you will design and implement a simple interface that can allow users to view and compare multimodal data on parent-child interaction easily.

Prerequisites: Required skills: Python, JavaScript, data visualization tools, data management tools

Time commitment: This position requires you to work at least 30 hours per week for the entire IAP period, and you may continue to work on it  during the Spring Semester

Relevant URL: https://www.media.mit.edu/groups/personal-robots/overview/

Contact: Huili Chen (hchen25@mit.edu)


11/15/18

Term: IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Marin Soljačić

Project Title: Could an off-the-shelf FPGA beat a state-of-the-art Quantum Computer?

Project Description: Over the course of the XXth and the beginning of the XXIst centuries, the long-standing goal of efficient computing architectures has been investigated by thousands of research teams. The community’s excitement was bolstered by the potential of massive parallelized computation, at an incredible speed and with very low-energy consumption. The last ten years have witnessed the rise of massive data processing techniques and hard optimization problems, mainly driven by their numerous applications in operations and scheduling, drug discovery, finance, circuit design, sensing, manufacturing and social media.

For these applications, the ability to process massive amount of data in parallel has urged on the development of new hardware, like GPUs for Deep Machine Learning implementation and FPGAs. While Moore’s law is petering out, the need for higher computation speed will keep soaring. The impossibility for conventional computers to solve large-scale hard optimization problems (called NP-hard problems) requires the development of application-oriented hardware. For instance, very intricate optical machines have been developed by the research community and by the industry (D-Wave). In this purpose, high-speed, low-power and massively parallel computation are required; three requirements for which FPGA architectures perform much better than their electrical counterpart.

In this project, the UROP student will develop a new scalable, low-power and parallel FPGA process architecture designed to efficiently solve NP-Hard problems. This system will mimic the optical architecture that has been recently developed by the research groups of Profs. Marin Soljačić and Dirk Englund. In addition to being a very hands-on project, with potential groundbreaking applications in computing, this work presents promising perspectives in application-oriented computing: if fully successful, our approach would enable a routing and delivering company like Amazon to reduce its carbon footprint by several orders of magnitude, or biochemists working on protein folding to decrease their simulation time from several hours today to less than a second and thus facilitate breakthrough discoveries in bioengineering and drug development.

The UROP will be supervised by a graduate student on a weekly basis and will ideally transition to leading this project and come up with new ideas.

Prerequisites: Experience with VHDL and/or FPGA hardware design.

Relevant URL: https://arxiv.org/abs/1811.02705 ; http://www.mit.edu/~soljacic/AI.html

Contact: Charles Roques Carmes (chrc@mit.edu)


11/14/18

IAP/Spring

UROP Department, Lab or Center: Center for Transportation & Logistics (CTL)

MIT Faculty Supervisor Name: Alexis Bateman

Project Title: The Sustainability Primacy Problem in Supply Chains

Project Description: Buyers and sellers who are linked together by shared supply chains do not always see and interpret ‘sustainability’ in the same way.  This research presents a new line of inquiry around this discord, which we call the Sustainability Primacy Problem.  The Sustainability Primacy Problem arises when trading partners see sustainability differently, and it is unclear to participants and/or observers whose prioritization of sustainability goals governs the interconnected supply chain. This project will analyze publicly available sustainability reports across supply chains to uncover how different companies prioritize sustainability differently.  Data will be collected using content analysis manually and if possible, automated. The student will work with two Research Scientists at the MIT Center for Transportation and Logistics, Drs. David Correll and Alexis Bateman to understand the spread of available information, code the information, and analyze it upon preliminary results.

Prerequisites: We are looking for someone who is strongly motivated and interested in the topic of study. The project is open to all relevant majors. No experience is required, but preferred skills include high reading comprehension, information coding, and statistical analysis. Hours are flexible.

Relevant URL: sustainable.mit.edu

Contact: David Correll: dcorrell@mit.edu


11/13/18

Term: IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Jesús del Alamo

Project Title: Artificial Synapse for Neuromorphic Computing

Project Description: For more than fifty years, dizzying progress in microelectronics and integrated circuits have revolutionized computing. Because of the recent unprecedented availability of computing power and data, a revolution in artificial intelligence has become possible. Machine learning algorithms, such as deep learning, involve large and deep neural networks, which require tremendous amounts of computation and are very power hungry. On the other hand, our brain is able to process complex information on a massively parallel scale while consuming only 1-100 fJ/synaptic event. For computing, there is much for us to learn from how the brain works.

Inspired by the brain, this project aims to study a new class of artificial synapses, called cation intercalation synapses, fabricated by the researchers at the groups of Prof. Jesús del Alamo at EECS and Prof. Bilge Yildiz at DMSE. The undergraduate researcher will contribute towards the development of electrical testing setups, both at the software and hardware level, to characterize, study and benchmark the new synaptic devices. A student interested in the intersection of fundamental solid-state device physics, electrical engineering, and computer science is particularly welcome to apply. This position is for IAP/Spring with potential for a longer-term commitment.

Prerequisites: We seek motivated students with strong interests in the interdisciplinary practice of microelectronic devices, circuits, and machine learning. Familiarity with Python or MATLAB are desired.

Contact: Wenjie Lu (wenjie@mit.edu)


11/13/18

Term: IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Pattie Maes

Project Title: Bio-Digital Wearables

Project Description: The MIT Media Lab’s Fluid Interfaces group designs seamless interfaces for human augmentation. Building upon research from neuroscience, biotechnology, and machine learning, the Fluid Interfaces group designs systems that help us exploit the untapped powers of human body to supplement our natural abilities to support attention, memory, emotion regulation, creativity, learning, decision making and more. The group designs wearable and immersive systems that enhance people's cognitive abilities to enable them to maximize their potential. We are seeking motivational UROP(s) who are interested in working on a novel bio-digital wearable and ubiquitous device for realtime/continuous digital sensing and monitoring of biomarkers. Our aim for the project is to develop a system that is capable of :

  • 1) Longitudinal/continuous monitoring of body biological data
  • 2) Plug and play system that can work with multiple biomarkers
  • 3) Easily adaptable bio-digital platform

You will be responsible for designing and testing the hardware arrangement. You will gain experience of working in an antidisciplinary setting: learn to integrate your work in field different from yours. This position is for Fall/IAP/Spring with potential for a longer term commitment.

Prerequisites: The project is open to students majoring in mechanical engineering, electrical engineering, computer science, and other relevant fields. The UROP must have solid experience in mechanical design and fabrication. Also experience in electronics, embedded programming or experience of working in biological science wet lab is preferable but not necessary.

Relevant URL: https://www.media.mit.edu/groups/fluid-interfaces/overview/

Contact: Pat Pataranutaporn (patpat@media.mit.edu)


11/13/18

Term: IAP

UROP Department, Lab or Center: BCS, Computational Psycholinguistics Lab

MIT Faculty Supervisor: Roger Levy

Project Title: Eye tracking for language processing

Project Description: We study how humans read and process language in real time using eye tracking technology. We are looking for a highly motivated student who is interested in language to join the project during IAP. As part of the UROP, you will learn about experimental techniques in psycholinguistics, and will be in charge of running a series of experiments using a state-of-the-art eyetracker.  Upon interest, there will be a possibility to continue the UROP into the Spring semester and participate in analyzing the collected data.

Prerequisites

  • - Responsible, independent, and highly attentive to detail.
  • - Preferably at least one semester of programming experience (e.g. 6.00/6.0001+6.0002).

Please include a CV and a copy of your transcript with your application.

Contact: Yevgeni Berzak (berzak@mit.edu)


11/13/18

Term: IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Langer

Project Title: App programming for medication information

Project Description: We are currently developing a new App to help patients and clinicians choose the right medications. The App should enable users to input medication names as well as harness image text recognition from pictures of medication packages to identify medications. The App will then automatically retrieve additional information about the medication. The project will involve programming the App for iOS and Android, testing different image recognition algorithms, implement data storage and processing, as well as data mining of public databases storing information on medications. Work will be conducted in the Langer Lab in the Koch Building for the Fall 2018.

Please send you resume/CV to Dr. Giovanni Traverso, cgt20@mit.edu, and Dr. Daniel Reker, reker@mit.edu

Prerequisites:

  • Previous experience with programming iOS and Android Apps.
  • Previous experience with image text recognition.
  • Motivated to work in a team and great communication skills.
  • At least 12 hours per week dedicated to the project work. IAP and Spring 2019.

Contact: Daniel Reker (reker@mit.edu)


11/13/18

Term: IAP/Spring

UROP Department, Lab or Center: MIT Quest for Intelligence (QI)

MIT Faculty Supervisor Name: Cynthia Breazeal

Project Title: Game Design for Collaborative Human Robot Interaction

Project Description: Personal Robots Group at MIT Media Lab is a part of the MIT Quest for Intelligence, and has been making efforts towards AI education for K-12 students. This project is about looking at how social robots can be used to foster creative thinking in elementary school children while they program AI agents. We are designing game frameworks, where the child teaches the robot how to play the games. We are looking for someone to help us build these platform strategy games on a web platform. Additionally, if interested, the researcher can be a part of evaluating the tools with children (If IAP + Spring).

Prerequisites: Experience with Javscript, preferably React/Pixi. Experience with Unity is a plus

Contact: Safinah Ali (safinah@media.mit.edu)


11/9/18

Term: IAP/Spring

UROP Department, Lab or Center: Electrical Engineering and Computer Science (Course 6)

MIT Faculty Supervisor Name: Yoel Fink

Project Title: Digital Fibers for Fabric Computing

Project Description: Logic is the ability in humans and computers enabling memory storage and decision processing. Incorporating logic functionality in fibers can thus empower highly intelligent fabrics. Here, we design and fabricate digital fibers with logic chips that can receive, process and send discrete signals, allowing information such as music, text and decision-making codes to be stored within the fabrics itself.  We are seeking UROPs to assist us in doing electrical characterization of our digital fibers, programming codes into these fibers and interfacing these digital fibers with other sensors for biomedical applications. The UROPs can expect to learn more about digital circuits, material studies on thermal drawing and electrical connections, and hardware programming with microcontrollers such as Arduino, PICAXE and Espruino.

Prerequisites: The project is open to students majoring in material engineering, electrical engineering, computer science, mechanical engineering, and other relevant fields. The UROP must have experience in coding (any languages such as Python, C, Javascript or others)

Relevant URL: http://www.rle.mit.edu/pbg/

Contact: Gabriel Loke (gabloke@mit.edu)


11/9/18

Term: IAP

UROP Department, Lab or Center: Economics (Course 14)

MIT Faculty Supervisor Name: Jon Gruber

Project Title: MIT Research to Public Policy Mapping

Project Description: The mission of the International Policy Lab (IPL) is to enhance the impact of MIT research on public policy. The IPL works with faculty from across the Institute to better connect their research to the decision makers who are able to act on it. As policy making becomes increasingly technical it will be vitally important that the knowledge created in Universities is properly applied to the policy making process.

In an effort to further develop such connections the IPL has begun an initiative to map all research conducted at MIT to corresponding policy domains. This project has three main goals. 1) To make MIT researchers more aware of the policy implications of their work, 2) to identify high impact opportunities for researchers to engage with policy makers, and 3) to facilitate the development of evidence-based policy both domestically and internationally.

This stage of this research project will focus on mapping out stakeholders in specific policy domains and developing a system of horizon scanning to identify policy opportunities.

Prerequisites: Ideal candidates will come from any School at MIT and will be comfortable drilling down to understand the technical details of multiple research programs. An interest in the application of research to the development of public policy is preferred but no policy experience is required. Training will be provided by MIT’s Washington DC office.

Relevant URL: http://policylab.mit.edu

Contact: Dan Pomeroy (dpomeroy@mit.edu)


11/9/18

Term: Spring/Summer

UROP Department, Lab or Center: Lincoln Laboratory (LL)

MIT Faculty Supervisor Name: Sumanth Kaushik

Project Title: Infrared Polarization, Astrophysical Dust, and Star-formation

Project Description: Polarized light is one of the few ways of remotely measuring magnetic fields and has implications for many fields of astrophysics including studies of the solar wind, star-formation, and the cosmic microwave background.  It is not well-understood how the polarized light traces magnetic fields, or in what types of physical environments the correspondence fails.  The current project will use new far-infrared data to study the dust-induced polarization in regions of active star-formation and compare it to other data sets tracing temperature, density, and interstellar chemistry.

Data sets that might be examined include (but are not limited to) polarization of starlight and emission from Galactic clouds, photometric imaging, and spectroscopic observations from several space- and ground-based observatories. Duties may include identifying relevant data sets, calibration of raw data, assisting in generation of computer models.

Prerequisites:

  • Programming experience in C/python/matlab/IDL or equivalent
  • Strong interest in Physics and/or Astronomy
  • Physics I and II (8.01x and 8.02x), or equivalent
  • Past or concurrent intro Astronomy course preferred (e.g., 8.28x)
  • Student in 2nd-year undergrad. or beyond, preferred
  • U.S. citizenship per LL security requirements

Relevant URL: https://sofia.usra.edu/public/news-updates/first-images-demonstrate-capabilities-sofia%E2%80%99s-new-instrument

To Apply: applicants should submit to the Lincoln Lab careers web-page (https://www.ll.mit.edu/careers).  Search for requisition 26018)

Contact: John Vaillancourt (john.vaillancourt@ll.mit.edu)


11/9/18

Term: IAP/Spring

UROP Department, Lab or Center: Media Laboratory

MIT Faculty Supervisor Name: Hugh Herr

Project Title: Neural Interfaces for Sensory and Motor Function

Project Description: Current limb prostheses for amputees do not allow one to control their limbs with their native neural signals. Non-invasive sensors have been developed to provide a control system, but none are highly sensitive. The biomechatronics lab is currently developing implantable devices to pair with a novel surgery that will enable patients to directly control prostheses and receive proprioceptive feedback. This project will involve the mechanical and electrical development of neural interfaces, their surgical implantation and testing. We will be performing surgeries in rats or mice, stimulating muscles and recording nervous signals. Then, we will characterize the muscular remodeling and physiological changes using histology and other techniques. For this multidisciplinary project, work will take place in the Media Lab and animal facilities with a team of mechanical engineers, biomedical engineers, electrical engineers, and clinicians. UROP will perform a variety of roles and learn medical device development, data collection and analysis as well as scientific writing.

Prerequisites: Previous experience with animal work, immunohistochemistry, or electronics would be very helpful - but not required.

Relevant URL: https://www.media.mit.edu/projects/electrical-interfaces/overview/

Contact: Shriya Srinivasan (shriyas@mit.edu)