Research

Impact Areas

Lead

Polina Golland

Research Group

Vision Group

Our researchers create state-of-the-art systems to better recognize objects, people, scenes, behaviors and more, with applications in health-care, gaming, tagging systems and more.

Leads

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Antonio Torralba

Research Areas

Impact Areas

Lead

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Antonio Torralba

Research Group

Geometric Data Processing Group

Our group studies geometric problems in computer graphics, computer vision, machine learning, optimization, and other disciplines.

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Lead

Justin Solomon

Research Center

Center for Deployable Machine Learning (CDML)

The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Lead

Aleksander Madry

Community of Research

Vertical AI Community of Research

This CoR takes a unified approach to cover the full range of research areas required for success in artificial intelligence, including hardware, foundations, software systems, and applications.

Martin Rinard

Leads

Martin Rinard

Research Areas

Impact Areas

Lead

Martin Rinard

Community of Research

Visual Computing Community of Research

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

+10

William Freeman

Leads

William Freeman

Research Areas

Impact Areas

Lead

William Freeman

+10

William Freeman

Research Group

Sensing, Learning and Inference

We develop scalable algorithms for analysis of complex, high-dimensional data.

John Fisher

Leads

John Fisher

Research Areas

Impact Areas

Energy

Internet of Things

Lead

John Fisher

12 Project Results matching all criteria

Project

Single-Image 3D Interpreter Network

We aim to understand 3D object structure from a single image. We propose an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, by training it on both real 2D-annotated images and synthetic 3D data and by integrating a 3D-to-2D projection layer.

Leads

Research Areas

Impact Areas

Project

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

In our work we developed a model that is able to synthesize many probable future frames with just a single image as input.

Leads

Research Areas

Project

Monitoring Fetal Health

We develop algorithms for fetal MRI interpretation, to enable noninvasive fetal monitoring from MRI.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

Project

3D Generative Adversarial Networks

We study the problem of 3D object generation. We propose a novel framework, 3D Generative Adversarial Network (3D-GAN), leveraging recent advances in volumetric convolutional networks and generative adversarial nets.

Leads

Research Areas

Impact Areas

Project

Reliable and Robust Machine Learning

Algorithms Group

We work towards a principled understanding of the current machine learning toolkit and making this toolkit be robust and reliable.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Aleksander Madry

Project

Geometry in Large-Scale Machine Learning

Data often has geometric structure which can enable better inference; this project aims to scale up geometry-aware techniques for use in machine learning settings with lots of data, so that this structure may be utilized in practice.

Justin Solomon

Stefanie Jegelka

Leads

Justin Solomon

Stefanie Jegelka

Research Areas

Impact Areas

Internet of Things

Justin Solomon

Stefanie Jegelka

Project

Inferring “Theory of Mind” with vision and language

Infolab

In this project, we aim to build a vision and language system which learns and understands the world like a child.

Boris Katz

Leads

Boris Katz

Research Areas

Boris Katz

Project

Cardiac MRI analysis

We develop image analysis algorithms for whole-heart cardiac MRI in patients with severe congenital heart disease.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

Project

Clinical Neuroimaging

We develop novel algorithms for neuroimage segmentation, registration, and joint modeling of brain structure with clinical and genetic data.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

Project

Quantification of Pulmonary Edema in Chest Radiographs

We develop machine learning algorithms to automatically and quantitatively assess the severity of pulmonary edema from chest x-ray images.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

Project

Sensible Deep Learning for 3D Data

Developing state-of-the-art deep learning algorithms for analyzing and modeling 3D geometry

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

Project

Geometry and topology for scientific computing and shape analysis

Developing state-of-the-art tools that process 3D surfaces and volumes

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

26 People Results matching all criteria

PI
Core/Dual

Ted Adelson

Professor

Vision, touch sensing, robotics

E adelson@csail.mit.edu

T 253-0645

Lead

Blood Pressure Imager

Member

Natural Language Processing Group

Vision, touch sensing, robotics

PI
Core/Dual

Regina Barzilay

Professor

Natural language processing, machine learning

E regina@csail.mit.edu

T 258-5706

Lead

Aspect-Augmented Adversarial Networks for Domain Adaptation

Member

Unsupervised Speech Processing

Natural language processing, machine learning

Yu-An Chung

Graduate Student

Speech and language processing, machine learning

E andyyuan@mit.edu

Lead

Member

Speech and language processing, machine learning

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

Member

HCI Engineering Group

PI
Core/Dual

Fredo Durand

Professor

Vision, graphics, computational photography

E fredo@csail.mit.edu

T 253-7223

Lead

Computational Understanding of Visualizations and Graphic Designs

Member

Computational Bounce Flash for Indoor Portraits

Halide

Vision, graphics, computational photography

PI
Core/Dual

John Fisher

Senior Research Scientist

Vision, machine learning, signal processing

E fisher@csail.mit.edu

T 253-0788

Lead

Sensing, Learning and Inference

Member

Vision, machine learning, signal processing

PI
Core/Dual

William Freeman

Professor

Vision, machine learning, Bayesian modeling

E billf@csail.mit.edu

T 253-8828

Lead

Single-Image 3D Interpreter Network

Member

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

Learning the Arrow of Time from Videos

3D Generative Adversarial Networks

Vision, machine learning, Bayesian modeling

PI
Core/Dual

Polina Golland

Professor

Vision, medical imaging, statistical inference

E polina@csail.mit.edu

T 253-8005

Lead

Quantification of Pulmonary Edema in Chest Radiographs

Cardiac MRI analysis

Clinical Neuroimaging

Member

Vision, medical imaging, statistical inference

Evan Hernandez

Graduate Student

Natural language processing, machine learning, interpretability

E dez@mit.edu

Natural language processing, machine learning, interpretability

PI
Core/Dual

Berthold Horn

Professor

Vision, computational imaging

E bkph@csail.mit.edu

T 253-5863

Lead

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Member

Vision, computational imaging

PI
Core/Dual

Leslie Kaelbling

Professor

Robotics, machine learning, motion planning

E lpk@csail.mit.edu

T 258-9695

Lead

Learning and Optimization

Robotic Planning and Manipulation with Force Information

Reliable Robotic Construction of Multi-object Arrangements

Learning and Planning with Compressed Linear Expectation Models

Active Learning of Models for Planning

Task and Motion Planning for Autonomous Robots

Member

Crowdsourcing in Graphics and Vision

Robotics, machine learning, motion planning

Alexandre Kaspar

Graduate Student

Graphics, crowdsourcing and hybrid fabrication.

E akaspar@mit.edu

Lead

Computational Knitting: Patterning and Shaping for Everyone

Graphics, crowdsourcing and hybrid fabrication.

Fern Keniston

Senior Administrative Assistant

Research and Education Support

E fern@csail.mit.edu

T 253-5860

RM 32/ 32-250A/ 391

Member

Research and Education Support

PI
Core/Dual

John Leonard

Professor

Robotics, navigation/mapping, self-driving cars

E jleonard@csail.mit.edu

T 253-5305

Lead

Marine Robotics Group

Self-Driving Vehicles

Member

Robotics, navigation/mapping, self-driving cars

Ruizhi Liao

Graduate Student

E ruizhi@mit.edu

Member

Quantification of Pulmonary Edema in Chest Radiographs

PI
Core/Dual

Tomas Lozano-Perez

Professor

Robotics, artificial intelligence

E tlp@csail.mit.edu

T 253-7889

Lead

Learning and Optimization

Robotic Planning and Manipulation with Force Information

Reliable Robotic Construction of Multi-object Arrangements

Learning and Planning with Compressed Linear Expectation Models

Active Learning of Models for Planning

Task and Motion Planning for Autonomous Robots

Member

Robotics, artificial intelligence

Hadi Salman

Graduate Student

EECS PhD Student joining in Spring 2021.

E hady@mit.edu

EECS PhD Student joining in Spring 2021.

Nalini Singh

Graduate Student

E nmsingh@mit.edu

RM 32-D475B

Member

Dmitriy Smirnov

Graduate Student

https://people.csail.mit.edu/smirnov/

E smirnov@mit.edu

RM 32-D475B

Member

Sensible Deep Learning for 3D Data

https://people.csail.mit.edu/smirnov/

PI
Core/Dual

Justin Solomon

Assistant Professor

Geometry, graphics, vision, machine learning, optimization.

E jsolomon@mit.edu

Lead

Geometry in Large-Scale Machine Learning

Political Geometry: Establishing Fair Mathematical Standards for Political Redistricting

Sensible Deep Learning for 3D Data

Optimal transport for statistics and machine learning

Geometry and topology for scientific computing and shape analysis

Member

Geometry, graphics, vision, machine learning, optimization.

Yunsheng Tian

Graduate Student

E yunsheng@mit.edu

PI
Core/Dual

Antonio Torralba

Professor

Vision, graphics, perception, machine learning

E torralba@csail.mit.edu

T 324-0900

RM 32-386G

Lead

Understanding Light via Deep Neural Networks

Understandable Deep Networks

Understanding Food images

Member

Predictive Vision

Where Are They Looking?

Single-Image 3D Interpreter Network

VirtualHome: Representing Activities as Programs

Places Database for Scene Recognition

Semantic Scene Understanding through ADE20K dataset

Crossing the Vision-Language Boundary

Center for Deployable Machine Learning (CDML)

Vision, graphics, perception, machine learning

Dishita Turakhia

Graduate Student

PhD student in HCIE group at CSAIL

E dishita@mit.edu

Member

HCI Engineering Group

PhD student in HCIE group at CSAIL

Yu Wang

Graduate Student

E wangyu9@mit.edu

RM 32-D475A

Member

Geometry and topology for scientific computing and shape analysis

23 News Results matching all criteria

AI & ML Graphics & Vision

January 13 '21

Professor Antonio Torralba elected 2021 AAAI Fellow

EECS faculty head of artificial intelligence and decision making honored for significant and extended contributions to the field of AI.

Professor Antonio Torralba elected 2021 AAAI Fellow

AI & ML Graphics & Vision

March 06 '20

A new model of vision

Computer model of face processing could reveal how the brain produces richly detailed visual representations so quickly.

A new model of vision

AI & ML Graphics & Vision

December 20 '19

Finding a good read among billions of choices

As natural language processing techniques improve, suggestions are getting speedier and more relevant.

Finding a good read among billions of choices

Algorithms & Theory AI & ML Graphics & Vision Big Data Transportation

October 29 '19

Deep learning with point clouds

Research aims to make it easier for self-driving cars, robotics, and other applications to understand the 3D world.

Deep learning with point clouds

Algorithms & Theory AI & ML Graphics & Vision Health Care

October 02 '19

Better fetal health - by building a map of the placenta

New technique stretches out MRI scans of placentas so they can be more accurately analyzed, and shows the potential of MRI for pregnancy monitoring.

Better fetal health - by building a map of the placenta

An MIT-invented algorithm automatically produces a “portamento” effect — a pitch sliding from one note to another — between any two audio signals in real time. The algorithm finds the optimal way to move pitches from one signal into another to produce a smooth transition between sounds (shown here).

AI & ML Graphics & Vision Systems & Networking Entertainment

September 27 '19

Using math to blend musical notes seamlessly

Algorithm enables one audio signal to glide into another, recreating the “portamento” effect of some musical instruments.

Using math to blend musical notes seamlessly

Algorithms & Theory AI & ML Graphics & Vision

August 22 '19

Why did my classifier just mistake a turtle for a rifle?

Two longtime friends explore how computer vision systems go awry.

Why did my classifier just mistake a turtle for a rifle?

Algorithms & Theory AI & ML Graphics & Vision Programming Languages & Software Engineering Systems & Networking Entertainment Manufacturing

August 05 '19

Computer-aided knitting

New CSAIL research uses machine learning to customize clothing designs

Computer-aided knitting

Algorithms & Theory AI & ML Computational Biology Computer Architecture Graphics & Vision Human-Computer Interaction Programming Languages & Software Engineering Robotics Security & Cryptography Systems & Networking Cybersecurity Education Energy Entertainment Health Care Manufacturing Transportation Wireless

June 27 '19

CSAIL hosts first-ever TEDxMIT

Speakers — all women — discuss everything from gravitational waves to robot nurses

CSAIL hosts first-ever TEDxMIT

AI & ML Graphics & Vision Systems & Networking Health Care Manufacturing

June 18 '19

Teaching artificial intelligence to connect senses like vision and touch

MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.

Teaching artificial intelligence to connect senses like vision and touch

Algorithms & Theory AI & ML Graphics & Vision Energy Entertainment Manufacturing

December 10 '18

Teaching machines to see in 3-D

CSAIL’s approach uses algorithms and “2.5-D” sketches to let computers visualize images from any perspective

Teaching machines to see in 3-D

Machines that learn language more like kids do 2

AI & ML Graphics & Vision Robotics

October 31 '18

Machines that learn language more like kids do

Computer model could improve human-machine interaction, provide insight into how children learn language.

Machines that learn language more like kids do

Algorithms & Theory AI & ML Graphics & Vision Health Care

September 19 '18

Machine-learning system tackles speech and object recognition, all at once

Model learns to pick out objects within an image, using spoken descriptions.

Machine-learning system tackles speech and object recognition, all at once

Algorithms & Theory AI & ML Graphics & Vision Robotics

September 17 '18

Helping computers fill in the gaps between video frames

Machine learning system efficiently recognizes activities by observing how objects change in only a few key frames.

Helping computers fill in the gaps between video frames

Algorithms & Theory AI & ML Graphics & Vision Entertainment

September 11 '18

Smoothing out sketches’ rough edges

MIT-developed tool improves automated image vectorization, saving digital artists time and effort.

Smoothing out sketches’ rough edges

AI & ML Graphics & Vision Entertainment

July 05 '18

An AI for editing music in videos

Given a video of a musical performance, CSAIL system can make specific instruments louder or softer

An AI for editing music in videos

Given an image of someone doing yoga, the system can generate an image of them doing golf (and vice versa)

AI & ML Graphics & Vision Education

June 18 '18

Put any person in any pose

MIT CSAIL neural network could be used by coaches to help players see themselves using the correct form

Put any person in any pose

AI & ML Graphics & Vision Health Care

June 12 '18

Artificial intelligence senses people through walls

Wireless smart-home system from the Computer Science and Artificial Intelligence Laboratory could monitor diseases and help the elderly “age in place.”

Artificial intelligence senses people through walls

Algorithms & Theory AI & ML Graphics & Vision Health Care

May 30 '18

Teaching chores to an artificial agent

Activity simulator could eventually teach robots tasks like making coffee or setting the table.

Teaching chores to an artificial agent

AI & ML Graphics & Vision Robotics Transportation

May 07 '18

Self-driving cars for country roads

For all the progress made in self-driving technologies, there still aren’t many places where they can actually drive. Companies like Google only test their fleets in major cities where they’ve spent countless hours meticulously labeling the exact 3-D positions of lanes, curbs, off-ramps, and stop signs.

Self-driving cars for country roads

A new system from MIT's Computer Science and Artificial Intelligence Laboratory works by analyzing light at the edge of walls, which is impacted by the reflections of objects around the corner from the camera.

AI & ML Graphics & Vision Transportation

October 09 '17

An algorithm for your blind spot

Light lets us see the things that surround us, but what if we could also use it to see things hidden around corners? It sounds like science fiction, but that’s the idea behind a new algorithm out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) — and its discovery has implications for everything from emergency response to self-driving cars.

An algorithm for your blind spot

System directs camera-equipped drones to maintain framing of an aerial shot.

Algorithms & Theory AI & ML Graphics & Vision Robotics Entertainment Transportation

May 17 '17

Cinematography on the fly

In recent years, a host of Hollywood blockbusters — including “The Fast and the Furious 7,” “Jurassic World,” and “The Wolf of Wall Street” — have included aerial tracking shots provided by drone helicopters outfitted with cameras. Those shots required separate operators for the drones and the cameras, and careful planning to avoid collisions. But a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable.

Cinematography on the fly

Humans and machines were given an image of a novel character (top) and asked to produce new versions. A machine generated the nine-character grid on the left.

AI & ML Graphics & Vision Systems & Networking

December 10 '15

Computer drawings fool human judges, pass “visual Turing test”

Researchers at CSAIL, New York University, and the University of Toronto have developed a computer system whose ability to produce a variation of a character in an unfamiliar writing system, on the first try, is indistinguishable from that of humans. That means that the system in some sense discerns what’s essential to the character — its general structure — but also what’s inessential — the minor variations characteristic of any one instance of it.

Computer drawings fool human judges, pass “visual Turing test”

32 Group Results

Research Group

Anyscale Learning for All ALFA

Our vision is data-driven machine learning systems that advance the quality of healthcare, the understanding of cyber arms races and the delivery of online education.

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Lead

Una-May O'Reilly

Community of Research

Applied Machine Learning Community of Research

This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Lead

Una-May O'Reilly

Research Group

Broderick Lab

We work in the areas of statistics and machine learning.

Tamara Broderick

Leads

Tamara Broderick

Research Areas

Impact Areas

Lead

Tamara Broderick

Research Group

Center for Brains, Minds and Machines

Our main goal is developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding.

Tomaso Poggio

Leads

Tomaso Poggio

Research Areas

Impact Areas

Lead

Tomaso Poggio

Research Center

Center for Deployable Machine Learning (CDML)

The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Lead

Aleksander Madry

Research Group

Clinical Decision-Making Group

We focus on furthering the application of technology and artificial intelligence in medicine and health-care.

+10

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Lead

Peter Szolovits

+10

Peter Szolovits

Research Group

Clinical Machine Learning Group

Our group is interested in using machine learning and artificial intelligence to transform health care.

David Sontag

Leads

David Sontag

Research Areas

Impact Areas

Lead

David Sontag

Research Group

Computational Cognitive Science Group

We study the computational basis of human learning and inference.

Joshua Tenenbaum

Leads

Joshua Tenenbaum

Research Areas

Impact Areas

Lead

Joshua Tenenbaum

Research Group

Computational Genomics Group

We develop new machine learning techniques and algorithms to model the transcriptional regulatory networks that control gene expression programs in living cells.

Dave Gifford

Leads

Dave Gifford

Research Areas

Impact Areas

Lead

Dave Gifford

Research Group

Computational Perception & Cognition

We combine methods from computer science, neuroscience and cognitive science to explain and model how perception and cognition are realized in human and machine.

Aude Oliva

Leads

Aude Oliva

Research Areas

Impact Areas

Lead

Aude Oliva

Research Group

Computer Graphics Group

Our group focuses on synthetic image generation, computational photography, digital manufacturing, and geometry.

Leads

Fredo Durand

William Freeman

Wojciech Matusik

Justin Solomon

Research Areas

Impact Areas

Lead

Fredo Durand

William Freeman

Wojciech Matusik

Justin Solomon

Community of Research

Computing & Society Community of Research

This community is interested in understanding and affecting the interaction between computing systems and society through engineering, computer science and public policy research, education, and public engagement.

Daniel Weitzner

Leads

Daniel Weitzner

Research Areas

Impact Areas

Lead

Daniel Weitzner

Research Group

Data-Driven Inference Group

We use machine learning and computer vision to improve outcomes in medicine, finance, and sports

John Guttag

Leads

John Guttag

Research Areas

Impact Areas

Lead

John Guttag

Research Group

Decentralized Information Group

We are investigating decentralized technologies that affect social change.

Lalana Kagal

Tim Berners-Lee

Leads

Lalana Kagal

Tim Berners-Lee

Research Areas

Impact Areas

Lead

Lalana Kagal

Tim Berners-Lee

Lalana Kagal

Tim Berners-Lee

Research Group

Distributed Robotics Laboratory

We aim to develop the science of autonomy toward a future with robots and AI systems integrated into everyday life, supporting people with cognitive and physical tasks.

+13

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

+13

Daniela Rus

Research Group

Genesis Group

The Genesis Group focuses on developing an account of human intelligence with special
emphasis on developing computational models of how people tell, perceive and understand
stories.

Leads

Research Areas

Impact Areas

Research Group

Geometric Data Processing Group

Our group studies geometric problems in computer graphics, computer vision, machine learning, optimization, and other disciplines.

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Lead

Justin Solomon

Community of Research

Human-Computer Interaction Community of Research

The focus of the HCI CoR is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

Stefanie Mueller

Leads

Stefanie Mueller

Research Areas

Impact Areas

Lead

Stefanie Mueller

Research Group

Imagination, Computation, and Expression Laboratory (ICE Lab)

We develop computing systems for creative expression, cultural analysis, and social change.

Peter Mawhorter

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Impact Areas

Lead

D. Fox Harrell

Peter Mawhorter

D. Fox Harrell

Research Group

Infolab

We investigate language in different contexts: from how it is learned, to how it is grounded in visual perception, all the way to how machines can readily interact with humans.

Boris Katz

Leads

Boris Katz

Research Areas

Impact Areas

Lead

Boris Katz

Research Center

Internet Policy Research Initiative

Our mission is to work with policy makers and cybersecurity technologists to increase the trustworthiness and effectiveness of interconnected digital systems.

Leads

Daniel Weitzner

Hal Abelson

David Clark

Lalana Kagal

Karen Sollins

Gerald Sussman

Research Areas

Impact Areas

Lead

Daniel Weitzner

Hal Abelson

David Clark

Lalana Kagal

Karen Sollins

Gerald Sussman

Research Group

Learning and Intelligent Systems

We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Lead

Leslie Kaelbling

Tomas Lozano-Perez

Leslie Kaelbling

Tomas Lozano-Perez

Research Group

Lecturers

Lecturers in CSAIL

Leads

Research Areas

Impact Areas

Research Group

Machine Learning

We work on a variety of topics spanning theoretical foundations, algorithms, and applications.

Leads

Tamara Broderick

Tommi Jaakkola

Stefanie Jegelka

Leslie Kaelbling

David Sontag

Research Areas

Impact Areas

Lead

Tamara Broderick

Tommi Jaakkola

Stefanie Jegelka

Leslie Kaelbling

David Sontag

101 Project Results

Project

3D Generative Adversarial Networks

Leads

Research Areas

Impact Areas

Computational Cognitive Science Group

Project

A Counterfactual Simulation Model of Causal Judgment

We develop a computational model that explains how people make causal judgments in physical scenes by mentally simulating counterfactual outcomes and comparing those to what actually happened.

Leads

Research Areas

Impact Areas

Project

Active Learning of Models for Planning

We aim to develop a systematic framework for robots to build models of the world and to use these to make effective and safe choices of actions to take in complex scenarios.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Anyscale Learning for All ALFA

Leslie Kaelbling

Tomas Lozano-Perez

Project

Adversarial Cyber Security

Our goal is to understand the nature of cyber security arms races between malicious and bonafide parties. Our vision is autonomous cyber defenses that anticipate and take measures against counter attacks.

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Imagination, Computation, and Expression Laboratory (ICE Lab)

Una-May O'Reilly

Project

AIRvatar System: A Framework for Analyzing Virtual Identities

Human-Computer Interaction Community of Research

AIRvatar is a system that telemetrically collects and analyzes fine-grained data on users’ virtual identities and the process used to create them.

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Impact Areas

Computer-Aided Programming

D. Fox Harrell

Project

An Interlock for Self Driving Cars

Software Design Group

Human-Computer Interaction Community of Research

Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.

Daniel Jackson

Armando Solar-Lezama

Leads

Daniel Jackson

Armando Solar-Lezama

Research Areas

Impact Areas

Daniel Jackson

Armando Solar-Lezama

Project

Analyzing Player Profile Data

Developing new methods for analyzing large quantities of player profile data, such as hundreds of thousands of profile images or chat logs.

D. Fox Harrell

Peter Mawhorter

Leads

D. Fox Harrell

Peter Mawhorter

Research Areas

Interactive Robotics Group

D. Fox Harrell

Peter Mawhorter

Project

Apprenticeship Scheduling: Learning to Schedule From Human Experts

Robot scheduling via learning from demonstration for tasks in medicine and more

Leads

Research Areas

Impact Areas

Project

Arabic Language Processing

The Arabic language is spoken by over one billion people around the world. Arabic presents a variety of challenges for speech and language processing technologies. In our group, we have several research topics examining Arabic, including dialect identification, speech recognition, machine translation, and language processing.

Mitra Mohtarami

Leads

Mitra Mohtarami

Research Areas

Mitra Mohtarami

Project

Artificial Co-driver

By observing driver in real road situations, we learn a computer assistant that can make driving easier, safer and more enjoyable.

Leads

Research Areas

Impact Areas

Weiss Laboratory for Synthetic Biology

Project

Artificial tissue homeostasis

In order to be able to design synthetic organs that function autonomously, we will need to engineer artificial tissue homeostasis. To control the size of these artificial tissues, two major mechanisms will have to be engineered.

Leads

Research Areas

Impact Areas

Natural Language Processing Group

Project

Aspect-Augmented Adversarial Networks for Domain Adaptation

We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.

Regina Barzilay

Tommi Jaakkola

Leads

Regina Barzilay

Tommi Jaakkola

Research Areas

Regina Barzilay

Tommi Jaakkola

Project

Automatic Speech Recognition

Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.

Sameer Khurana

Jim Glass

Leads

Sameer Khurana

Jim Glass

Research Areas

Impact Areas

Sameer Khurana

Jim Glass

Project

Autonomous Vehicle Databases

Data Systems Group

The impending success of AV technology will create vehicles that collect sensor data at a high rate.

Michael Stonebraker

Sam Madden

Leads

Michael Stonebraker

Sam Madden

Research Areas

Impact Areas

Michael Stonebraker

Sam Madden

Project

AWE-CM Vectors: Augmenting Word Embeddings with a Clinical Metathesaurus

Adding domain knowledge to word embeddings.

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Peter Szolovits

Project

Bayesian Optimization for Global Optimization of Expensive Black-box Functions

We study the fundamentals of Bayesian optimization and develop efficient Bayesian optimization methods for global optimization of expensive black-box functions originated from a range of different applications.

Leads

Research Areas

Impact Areas

Project

Better Models for Ride-Sharing

Traffic is not just a nuisance for drivers: It’s also a public health hazard and bad news for the economy.

Leads

Research Areas

Impact Areas

Energy

Project

Blood Pressure Imager

Development of affordable wearable continuous blood pressure monitor based on radial arterial pulse imaged from the skin surface of human wrist

Mumin Jin

Aya G. Halawi

Jianhua Li

Leads

Abhijit Biswas

Ted Adelson

Mandayam A. Srinivasan

Mohan Thanikachalam

Research Areas

Decentralized Information Group

Mumin Jin

Aya G. Halawi

Jianhua Li

Project

The Car Can Explain!

Computing & Society Community of Research

Developing techniques to allow self-driving cars and other AI-driven systems to explain behaviors and failures.

Gerald Sussman

Lalana Kagal

Leads

Gerald Sussman

Lalana Kagal

Research Areas

Impact Areas

Gerald Sussman

Lalana Kagal

Project

Cardiac MRI analysis

We develop image analysis algorithms for whole-heart cardiac MRI in patients with severe congenital heart disease.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

Project

Characterizing the Sensitivity of Vision-in-the-Loop Driving Controllers Using CG

We work on understanding and improving vision algorithms used for self driving cars, by introducing synthetic data-sets to the loop.

Leads

Research Areas

Impact Areas

Project

CliNER: Clinical Concept Extraction

Clinical concept extraction (CCE) of named entities - such as problems, tests, and treatments - aids in forming an understanding of notes and provides a foundation for many downstream clinical decision-making tasks. Historically, this task has been posed as a standard named entity recognition (NER) sequence tagging problem, and solved with feature-based methods using hand-engineered domain knowledge. Recent advances, however, have demonstrated the efficacy of LSTM-based models for NER tasks, including CCE. This work presents CliNER 2.0, a simple-to-install, open-source tool for extracting concepts from clinical text. CliNER 2.0 uses a word- and character- level LSTM model, and achieves state-of-the-art performance. For ease of use, the tool also includes pre-trained models available for public use.

Leads

William Boag

Elena Sergeeva

Peter Szolovits

Anna Rumshisky

Research Areas

Impact Areas

Energy

Project

Clinical Intervention Prediction with Neural Networks

Integrating multi-modal clinical data and using recurrent and convolution neural networks to predict when patients will need important interventions.

Leads

Research Areas

Impact Areas

Project

Clinical Neuroimaging

We develop novel algorithms for neuroimage segmentation, registration, and joint modeling of brain structure with clinical and genetic data.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

111 People Results

Mazdak Abulnaga

Graduate Student

medical vision

E abulnaga@mit.edu

Member

Geometry and topology for scientific computing and shape analysis

medical vision

PI
Core/Dual

Ted Adelson

Professor

Vision, touch sensing, robotics

E adelson@csail.mit.edu

T 253-0645

Lead

Blood Pressure Imager

Member

Robust Learning for Autonomous Vehicles

Vision, touch sensing, robotics

Monica Agrawal

Graduate Student

E magrawal@mit.edu

Leandro Agudelo

Research Scientist

Systems Genomics, CompBio, Topological Integrative Biology, Biology

E lagudelo@csail.mit.edu

Systems Genomics, CompBio, Topological Integrative Biology, Biology

Ekin Akyürek

Graduate Student

First year PhD student in computer science

E akyurek@mit.edu

First year PhD student in computer science

Alexander Amini

Graduate Student

PhD Candidate. Deep Learning; Robotics

E Amini@mit.edu

Lead

Member

Self-Driving Vehicles

Natural Language Processing Group

PhD Candidate. Deep Learning; Robotics

Brandon Araki

Graduate Student

E araki@csail.mit.edu

Anish Athalye

Graduate Student

PhD student in PDOS

E aathalye@mit.edu

RM 32-G978A

PhD student in PDOS

PI
Core/Dual

Regina Barzilay

Professor

Natural language processing, machine learning

E regina@csail.mit.edu

T 258-5706

Lead

Aspect-Augmented Adversarial Networks for Domain Adaptation

Member

Natural language processing, machine learning

Cenk Baykal

Graduate Student

E baykal@mit.edu

RM 32-376C

Member

Coresets for Machine Learning Algorithms

Laurent Bindschaedler

Postdoctoral Fellow

Systems for Big Data and ML. ML for Systems.

E bindscha@mit.edu

Member

Data Systems Group

Systems for Big Data and ML. ML for Systems.

William Boag

Graduate Student

Clinical NLP

E wboag@mit.edu

Lead

Synthetically-Identified Clinical Notes

Information Retrieval for Cancer Treatments in Clinical Literature and Trial Eligibility

CliNER: Clinical Concept Extraction

Quantifying Racial Disparities in End-of-Life Care

Member

Clinical NLP

PI
Core/Dual

Tamara Broderick

Associate Professor

Machine learning, statistics, Bayesian methods, inference

E tbroderick@csail.mit.edu

T 324-6749

Lead

Scalable Bayesian Inference via Adaptive Data Summaries

Scalable Bayesian inference with optimization

Broderick Lab

Member

Machine learning, statistics, Bayesian methods, inference

Noam Buckman

Graduate Student

PhD Candidate. Autonomous vehicles. Multi-robot systems.

E nbuckman@mit.edu

Member

Model-Based Embedded and Robotics Systems Group

Self-Driving Vehicles

PhD Candidate. Autonomous vehicles. Multi-robot systems.

Richard Camilli

Research Affiliate

E romeo@mit.edu

Member

Geeticka Chauhan

Graduate Student

E geeticka@mit.edu

Member

Quantification of Pulmonary Edema in Chest Radiographs

Irene Chen

Graduate Student

Machine learning, healthcare, fairness

E iychen@mit.edu

Member

Clinical Machine Learning Group

Unsupervised Speech Processing

Machine learning, healthcare, fairness

Yu-An Chung

Graduate Student

Speech and language processing, machine learning

E andyyuan@mit.edu

Lead

Member