Research

Impact Areas

Lead

Una-May O'Reilly

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 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

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

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.

+15

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

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

Imagination, Computation, and Expression Laboratory (ICE Lab)

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

Peter Mawhorter

James Bowie Wilson

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Impact Areas

Lead

D. Fox Harrell

Peter Mawhorter

James Bowie Wilson

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 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

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

Research Group

Medical Vision Group

We develop new algorithms for medical image analysis and visualization of medical imagery.

+10

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Lead

Polina Golland

Polina Golland

Research Group

Natural Language Processing Group

Our research encompasses all aspects of NLP, from modeling basic linguistic phenomena to designing practical text processing systems, and developing new machine learning methods.

Regina Barzilay

Tommi Jaakkola

Leads

Regina Barzilay

Tommi Jaakkola

Research Areas

Impact Areas

Lead

Regina Barzilay

Tommi Jaakkola

Regina Barzilay

Tommi Jaakkola

Research Group

Spoken Language Systems Group

Our goal is to create technology that makes it possible for everyone in the world to interact with with computers via natural spoken language.

+15

Jim Glass

Leads

Jim Glass

Research Areas

Impact Areas

Lead

Jim Glass

Jim Glass

Research Group

Sussman Lab

We focus on understanding the problem-solving strategies used by scientists and engineers, with the goals of automating parts of the process and formalizing educational methods.

Gerald Sussman

Leads

Gerald Sussman

Research Areas

Impact Areas

Lead

Gerald Sussman

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

Ruth Rosenholtz

Antonio Torralba

Research Areas

Impact Areas

Lead

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Ruth Rosenholtz

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

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

Sensing, Learning and Inference

We focus on the analysis of complex, high-dimensional data.

John Fisher

Leads

John Fisher

Research Areas

Impact Areas

Internet of Things

Lead

John Fisher

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

62 Project Results matching all criteria

Project

Using Computers to Eat Healthier

Our goal is to build an AI-powered personal digital nutritionist that enables users to track the food they eat simply by speaking or typing natural English phrases.

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

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

Learning Strategic Games

Machine Learning

Our goal is to develop new tools for modeling diverse multi-agent settings, and design estimation algorithms to unravel the strategic interactions among the agents.

Vikas Garg

Tommi Jaakkola

Leads

Vikas Garg

Tommi Jaakkola

Research Areas

Decentralized Information Group

Vikas Garg

Tommi Jaakkola

Project

The Car Can Explain!

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

Leads

Gerald Sussman

Lalana Kagal

Daniel Weitzner

Research Areas

Impact Areas

Project

Who Knows What? Perspective-Enabled Story Understanding

Genesis Group

To understand human intelligence, we must be able to model how humans understand stories from multiple characters' perspectives.

Leads

Research Areas

Impact Areas

Data-Driven Inference Group

Project

Predicting Adverse Events Across Changing Electronic Health Record Systems

Transitioning machine learning models across electronic health record (EHR) versions can be improved by mapping different EHR encodings to a common vocabulary.

John Guttag

Peter Szolovits

Leads

John Guttag

Peter Szolovits

Research Areas

Impact Areas

John Guttag

Peter Szolovits

Project

Understanding Language Representations in Deep Learning Models

Our goal is to explore language representations in computational models. We develop new models for representing natural language and investigate how existing models learn language, focusing on neural network models in key tasks like machine translation and speech recognition.

Yonatan Belinkov

Leads

Yonatan Belinkov

Research Areas

Yonatan Belinkov

Project

SoFi - The Soft Robotic Fish

Exploration of underwater life with an acoustically controlled soft robotic fish

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Impact Areas

Imagination, Computation, and Expression Laboratory (ICE Lab)

Joseph DelPreto

Project

Toward Using Virtual Identities in Computer Science Learning for Broadening Participation

Our research seeks to discover best practices for using avatars to enhance performance, engagement, and STEM identity development for diverse public middle and high school computer science students. As sites of our research we run workshops in which students learn computer science in fun, relevant ways, and develop self-images as computer scientists.

Sneha Veeragoudar Harrell

Aziria Rodriguez

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Impact Areas

Sneha Veeragoudar Harrell

Aziria Rodriguez

D. Fox Harrell

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

Detecting Voice Misuse to Diagnose Disorders

Our team uses accelerometers and machine learning to help detect vocal disorders. We capture data about the motions of patient's vocal folds to determine if their vocal behavior is normal or abnormal.

John Guttag

Leads

John Guttag

Research Areas

Impact Areas

John Guttag

Project

Multivariate Signal Representations That Predict ICU Interventions

Our focus is using computational tools to summarize health-related data to help clinicians focus on decision-making rather than just keeping up with the data.

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Peter Szolovits

Project

Clinical Note Representations That Predict ICU Mortality/Re-Admission

Our focus is on improving health-related outcomes by creating machine learning models that improve human risks.

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Weiss Laboratory for Synthetic Biology

Peter Szolovits

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

Project

MOOC Learner Project: Data science for e-learning

The MOOC Learner Project provides learning scientists, instructional designers and online education specialists with open source software that enables them to efficiently extract teaching and learning insights from the data collected when students learn on the edX or open edX platform.

Una-May O'Reilly

Erik Hemberg

Leads

Una-May O'Reilly

Erik Hemberg

Research Areas

Impact Areas

Una-May O'Reilly

Erik Hemberg

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

Una-May O'Reilly

Project

GIGABEATS: Data science for medical sensor data

Our goal is to help medical researchers and clincians understand the growing repositories of waveform and signal data collected from critically ill patients.

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Una-May O'Reilly

Project

Drones that Drive

Multi-robot path planning for robot swarms that can both fly and drive

Brandon Araki

Leads

Brandon Araki

Research Areas

Impact Areas

Interactive Robotics Group

Brandon Araki

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

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

8 People Results matching all criteria

Azim Dehghani Amirabad

Postdoctoral Associate

Machine learning, Bioinformatics

E azimd@csail.mit.edu

Machine learning, Bioinformatics

Francois Grondin

Postdoctoral Fellow

Postdoctoral Research Associate

E fgrondin@csail.mit.edu

Member

Fact-Checking and Reasoning

Postdoctoral Research Associate

Ryan Marcus

Postdoctoral Associate

Machine learning for systems

E ryanmarcus@csail.mit.edu

Member

Data Systems Group

Machine learning for systems

Mitra Mohtarami

Research Scientist

E mitra@csail.mit.edu

Lead

Community Question Answering

Arabic Language Processing

Member

Speaker Verification and Diarization

Markus Ryll

Postdoctoral Associate

E ryll@csail.mit.edu

RM 32-334

Suwon Shon

Research Scientist

E swshon@mit.edu

Lead

Language and Dialect Identification

Member

Arabic Language Processing

Neil Thompson

Research Scientist

I'm an innovation scholar studying computer science and economics.

E neil_t@mit.edu

I'm an innovation scholar studying computer science and economics.

Longfei Zhou

Postdoctoral Fellow

Postdoc Fellow, computer vision, MIT.

E longfei@csail.mit.edu

Postdoc Fellow, computer vision, MIT.

90 News Results matching all criteria

Algorithms & Theory AI & Machine Learning Robotics

November 13 '19

Autonomous system improves environmental sampling at sea

Robotic boats could more rapidly locate the most valuable sampling spots in uncharted waters.

Autonomous system improves environmental sampling at sea

AI & Machine Learning Robotics Manufacturing Transportation

October 30 '19

Self-transforming robot blocks jump, spin, flip, and identify each other

Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, a team of robots can self-assemble to form different structures with applications in inspection, disaster response, and manufacturing

Self-transforming robot blocks jump, spin, flip, and identify each other

Algorithms & Theory AI & Machine Learning

October 29 '19

Pushy robots learn the fundamentals of object manipulation

Systems “learn” from novel dataset that captures how pushed objects move, to improve their physical interactions with new objects.

Pushy robots learn the fundamentals of object manipulation

Algorithms & Theory AI & Machine Learning Robotics

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

AI & Machine Learning

October 24 '19

Better fact-checking for fake news

New research reveals biases in fake news datasets and improves the use of automatic detectors.

Better fact-checking for fake news

AI & Machine Learning Health Care

September 12 '19

Using machine learning to estimate risk of cardiovascular death

Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, new system uses a patient's ECG signal to estimate potential for cardiovascular death

Using machine learning to estimate risk of cardiovascular death

Algorithms & Theory AI & Machine Learning Robotics Transportation

August 29 '19

MIT’s fleet of autonomous boats can now shapeshift

New capabilities allow “roboats” to change configurations to form pop-up bridges, stages, and other structures.

MIT’s fleet of autonomous boats can now shapeshift

Algorithms & Theory AI & Machine Learning Energy

August 28 '19

Artificial intelligence could help data centers run far more efficiently

MIT system “learns” how to optimally allocate workloads across thousands of servers to cut costs, save energy.

Artificial intelligence could help data centers run far more efficiently

Algorithms & Theory AI & Machine Learning 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 & Machine Learning 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

AI & Machine Learning Robotics Health Care

July 22 '19

Prime Minister Mark Rutte of the Netherlands tours MIT

Dutch delegation visits the Institute for a tour focused on computing, robotics, and health care innovation.

Prime Minister Mark Rutte of the Netherlands tours MIT

AI & Machine Learning Education

July 22 '19

Professor Patrick Winston, former director of MIT’s Artificial Intelligence Laboratory, dies at 76

Beloved professor conducted pioneering research on imbuing machines with human-like intelligence, including the ability to understand stories.

Professor Patrick Winston, former director of MIT’s Artificial Intelligence Laboratory, dies at 76

Algorithms & Theory AI & Machine Learning 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

Algorithms & Theory AI & Machine Learning Health Care

June 27 '19

Drag-and-drop data analytics

System lets nonspecialists use machine-learning models to make predictions for medical research, sales, and more.

Drag-and-drop data analytics

Algorithms & Theory AI & Machine Learning Health Care

June 25 '19

From one brain scan, more information for medical artificial intelligence

System helps machine-learning models glean training information for diagnosing and treating brain conditions Home Node From one brain scan, more information for medical artificial intelligence

From one brain scan, more information for medical artificial intelligence

AI & Machine Learning 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 & Machine Learning

June 04 '19

Q&A: Phillip Isola on the art and science of generative models

Image-translation pioneer discusses the past, present, and future of generative adversarial networks, or GANs.

Q&A: Phillip Isola on the art and science of generative models

AI & Machine Learning Robotics Manufacturing

May 22 '19

This robot helps you lift objects — by looking at your biceps

CSAIL system can mirror a user's motions and follow nonverbal commands by monitoring arm muscles.

This robot helps you lift objects — by looking at your biceps

AI & Machine Learning Computational Biology Health Care

May 07 '19

Using AI to predict breast cancer and personalize care

Image-based AI model can predict breast cancer years in advance

Using AI to predict breast cancer and personalize care

AI & Machine Learning Education

April 29 '19

Preparing high schoolers for a tech-driven future

MIT researchers joined Cambridge Rindge and Latin School students for a two-day event focused on future-ready skills.

Preparing high schoolers for a tech-driven future

AI & Machine Learning Robotics Energy Health Care

April 18 '19

Robots that can sort recycling

CSAIL’s "RoCycle" system uses in-hand sensors to detect if an object is paper, metal or plastic.

Robots that can sort recycling

Algorithms & Theory AI & Machine Learning

March 22 '19

“Particle robot” works as a cluster of simple units

Loosely connected disc-shaped “particles” can push and pull one another, moving en masse to transport objects.

“Particle robot” works as a cluster of simple units

AI & Machine Learning Robotics Health Care Manufacturing

March 14 '19

New robot hand is soft and strong

Gripper inspired by “origami magic ball” can grasp wide array of delicate and heavy objects

New robot hand is soft and strong

AI & Machine Learning

March 06 '19

Computing the future

Fireside chat brings together six Turing Award winners to reflect on their field and the MIT Stephen A. Schwarzman College of Computing.

Computing the future

25 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

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 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

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

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.

+15

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

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

Research Group

Imagination, Computation, and Expression Laboratory (ICE Lab)

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

Peter Mawhorter

James Bowie Wilson

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Impact Areas

Lead

D. Fox Harrell

Peter Mawhorter

James Bowie Wilson

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

Research Group

Medical Vision Group

We develop new algorithms for medical image analysis and visualization of medical imagery.

+10

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Lead

Polina Golland

Polina Golland

Research Group

Natural Language Processing Group

Our research encompasses all aspects of NLP, from modeling basic linguistic phenomena to designing practical text processing systems, and developing new machine learning methods.

Regina Barzilay

Tommi Jaakkola

Leads

Regina Barzilay

Tommi Jaakkola

Research Areas

Impact Areas

Lead

Regina Barzilay

Tommi Jaakkola

Regina Barzilay

Tommi Jaakkola

Research Group

Sensing, Learning and Inference

We focus on the analysis of complex, high-dimensional data.

John Fisher

Leads

John Fisher

Research Areas

Impact Areas

Internet of Things

Lead

John Fisher

Research Group

Spoken Language Systems Group

Our goal is to create technology that makes it possible for everyone in the world to interact with with computers via natural spoken language.

+15

Jim Glass

Leads

Jim Glass

Research Areas

Impact Areas

Lead

Jim Glass

Jim Glass

Research Group

Sussman Lab

We focus on understanding the problem-solving strategies used by scientists and engineers, with the goals of automating parts of the process and formalizing educational methods.

Gerald Sussman

Leads

Gerald Sussman

Research Areas

Impact Areas

Lead

Gerald Sussman

62 Project Results

Project

3D Generative Adversarial Networks

Leads

Research Areas

Impact Areas

Learning and Intelligent Systems

Project

Active Learning of Models for Planning

Machine Learning

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.

Leads

Zi Wang

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Project

Adversarial Cyber Security

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Computer-Aided Programming

Una-May O'Reilly

Project

An Interlock for Self Driving Cars

Software Design Group

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

Interactive Robotics Group

Daniel Jackson

Armando Solar-Lezama

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.

Leads

Ramy Baly

Yonatan Belinkov

Mitra Mohtarami

Research Areas

Weiss Laboratory for Synthetic Biology

Project

Artificial tissue homeostasis

Leads

Research Areas

Impact Areas

Natural Language Processing Group

Project

Aspect-Augmented Adversarial Networks for Domain Adaptation

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.

Leads

Hao Tang

Wei-Ning Hsu

David Harwath

Sameer Khurana

Di-Chia Chueh

Jim Glass

Research Areas

Impact Areas

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

Learning and Intelligent Systems

Peter Szolovits

Project

Bayesian Optimization for Global Optimization of Expensive Black-box Functions

Machine Learning

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.

Zi Wang

Leads

Zi Wang

Research Areas

Impact Areas

Zi Wang

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

Decentralized Information Group

Project

The Car Can Explain!

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

Leads

Gerald Sussman

Lalana Kagal

Daniel Weitzner

Research Areas

Impact Areas

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

Chest X-Ray Analysis

We develop machine learning algorithms to automatically quantify the severity of pulmonary edema from chest x-rays.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

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

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

Project

Clinical Note Representations That Predict ICU Mortality/Re-Admission

Our focus is on improving health-related outcomes by creating machine learning models that improve human risks.

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Peter Szolovits

Project

Community Question Answering