Research

  • Impact Areas
  • Research Areas

Algorithms & Theory

Foundational work that includes complexity, parallel computing and game theory

AI & ML

Spanning natural language processing, deep-learning, computer vision and more

Computational Biology

Understanding disease via epigenomics, gene regulation and bioinformatics

Computer Architecture

How CPUs, memory and other systems are designed and organized

Graphics & Vision

Teaching computers to interpret, create and animate visual data

Human-Computer Interaction

Software and hardware that let us naturally interact with technology

Programming Languages & Software Engineering

From compilers and verification to software design and engineering

Robotics

Vision, actuation, sensing and manipulation of machines

Security & Cryptography

Developing technologies to prevent and recover from cyber-attacks

Systems & Networking

From distributed systems and databases to wireless

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28 Group Results

Research Group

Algorithms Group

We devise new mathematical tools to tackle the increasing difficulty and importance of problems we pose to computers.

Leads

Daskalakis

Costis Daskalakis

Erik Demaine

Erik Demaine

Goemans

Michael Goemans

Indyk

Piotr Indyk

Karger

David Karger

Jonathan Kelner

Jonathan Kelner

Leighton

Tom Leighton

Professor Charles E. Leiserson

Charles E. Leiserson

Madry-headshot

Aleksander Madry

Moitra-headshot

Ankur Moitra

Rubinfeld

Ronitt Rubinfeld

shavit-headshot

Nir Shavit

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

Research Areas

Algorithms & Theory

Impact Areas

Big Data
Cybersecurity
Energy
Wireless

Lead

Daskalakis

Costis Daskalakis

Erik Demaine

Erik Demaine

Goemans

Michael Goemans

Indyk

Piotr Indyk

Karger

David Karger

Jonathan Kelner

Jonathan Kelner

Leighton

Tom Leighton

Professor Charles E. Leiserson

Charles E. Leiserson

Madry-headshot

Aleksander Madry

Moitra-headshot

Ankur Moitra

Rubinfeld

Ronitt Rubinfeld

shavit-headshot

Nir Shavit

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

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.
Daskalakis
Moitra-headshot
Rinard
+5
Madry-headshot

Aleksander Madry

Leads

Madry-headshot

Aleksander Madry

Research Areas

Algorithms & Theory
AI & ML
Graphics & Vision
Human-Computer Interaction
Programming Languages & Software Engineering
Robotics
Security & Cryptography

Impact Areas

Big Data
Cybersecurity

Lead

Madry-headshot

Aleksander Madry

Daskalakis
Moitra-headshot
Rinard
+5
 
Madry-headshot

Aleksander Madry

Research Group

Complexity Theory Group

Our interests span quantum complexity theory, barriers to solving P versus NP, theoretical computer science with a focus on probabilistically checkable proofs (PCP), pseudo-randomness, coding theory, and algorithms.

Leads

Goldwasser

Shafi Goldwasser

Rubinfeld

Ronitt Rubinfeld

Sipser

Michael Sipser

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

Research Areas

Algorithms & Theory

Impact Areas

Big Data
Cybersecurity

Lead

Goldwasser

Shafi Goldwasser

Rubinfeld

Ronitt Rubinfeld

Sipser

Michael Sipser

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

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.
Abelson
Berners-Lee
Brewer-headshot
+8
Weitzner

Daniel Weitzner

Leads

Weitzner

Daniel Weitzner

Research Areas

Algorithms & Theory
AI & ML
Human-Computer Interaction
Security & Cryptography
Systems & Networking

Impact Areas

Big Data
Cybersecurity
Health Care
Internet of Things
Transportation
Wireless

Lead

Weitzner

Daniel Weitzner

Abelson
Berners-Lee
Brewer-headshot
+8
 
Weitzner

Daniel Weitzner

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.
default headshot
default headshot
default headshot
+2

Leads

Weitzner

Daniel Weitzner

Abelson

Hal Abelson

Clark

David Clark

Kagal-headshot

Lalana Kagal

Karen Sollins

Karen Sollins

Gerald Sussman

Gerald Sussman

Research Areas

AI & ML
Human-Computer Interaction
Security & Cryptography

Impact Areas

Big Data
Cybersecurity
Internet of Things
Transportation

Lead

Weitzner

Daniel Weitzner

Abelson

Hal Abelson

Clark

David Clark

Kagal-headshot

Lalana Kagal

Karen Sollins

Karen Sollins

Gerald Sussman

Gerald Sussman

default headshot
default headshot
default headshot
+2

Community of Research

Systems Community of Research

The Systems CoR is focused on building and investigating large-scale software systems that power modern computers, phones, data centers, and networks, including operating systems, the Internet, wireless networks, databases, and other software infrastructure.
Alizadeh-headshot
Arvind
Hari Balakrishnan
+21
Madden

Sam Madden

Leads

Madden

Sam Madden

Research Areas

Computer Architecture
Programming Languages & Software Engineering
Security & Cryptography
Systems & Networking

Impact Areas

Big Data
Cybersecurity
Internet of Things
Transportation
Wireless

Lead

Madden

Sam Madden

Alizadeh-headshot
Arvind
Hari Balakrishnan
+21
 
Madden

Sam Madden

39 Project Results

Project

Active Learning of Models for Planning

Learning and Intelligent Systems
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.
Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Leads

Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Research Areas

Algorithms & Theory
AI & ML
Human-Computer Interaction
Robotics

Impact Areas

Big Data
Education
Entertainment
 
Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Project

Alloy

Software Design Group
Human-Computer Interaction Community of Research
Alloy is a language for describing structures and a tool for exploring them. It has been used in a wide range of applications from finding holes in security mechanisms to designing telephone switching networks. Hundreds of projects have used Alloy for design analysis, for verification, for simulation, and as a backend for many other kinds of analysis and synthesis tools, and Alloy is currently being taught in courses worldwide.
Jackson image

Daniel Jackson

Leads

Jackson image

Daniel Jackson

Research Areas

Human-Computer Interaction
Programming Languages & Software Engineering
Security & Cryptography

Impact Areas

Big Data
Cybersecurity
Education
Transportation
Wireless
 
Jackson image

Daniel Jackson

Project

Automatic Speech Recognition

Spoken Language Systems Group
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.
MIT CSAIL Spoken Language Systems PhD student

Sameer Khurana

Jim Glass

Jim Glass

Leads

MIT CSAIL Spoken Language Systems PhD student

Sameer Khurana

Jim Glass

Jim Glass

Research Areas

AI & ML
Human-Computer Interaction

Impact Areas

Big Data
Education
Entertainment
Health Care
Internet of Things
 
MIT CSAIL Spoken Language Systems PhD student

Sameer Khurana

Jim Glass

Jim Glass

Project

CliNER: Clinical Concept Extraction

Cognitive AI Community of Research
Applied Machine Learning Community of Research
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

Willie

William Boag

Elena Sergeeva

Elena Sergeeva

Szolovits

Peter Szolovits

default headshot

Anna Rumshisky

Research Areas

AI & ML

Impact Areas

Energy

Project

Determining Wikipedia's Influence on Science

Supertech Research Group
Wikipedia is one of the most widely accessed encyclopedia sites in the world, including by scientists. Our project aims to investigate just how far Wikipedia’s influence goes in shaping science.
Neil Thompson Headshot

Neil Thompson

Professor Charles E. Leiserson

Charles E. Leiserson

Leads

Neil Thompson Headshot

Neil Thompson

Professor Charles E. Leiserson

Charles E. Leiserson

Research Areas

Algorithms & Theory
Human-Computer Interaction
Programming Languages & Software Engineering

Impact Areas

Big Data
Education
 
Neil Thompson Headshot

Neil Thompson

Professor Charles E. Leiserson

Charles E. Leiserson

62 People Results

R. David Edelman - Headshot

R. David Edelman

Director, Project on Technology, Economy & National Security
Machine learning, cybersecurity, governance
R. David Edelman - Headshot
T 258-6389
Machine learning, cybersecurity, governance
Gregory Falco

Gregory Falco

Research Affiliate
Dr. Falco is a hacker and expert in IoT and IIoT cybersecurity.
Gregory Falco
Dr. Falco is a hacker and expert in IoT and IIoT cybersecurity.
Jim Glass

PI
Core/Dual

Jim Glass

Senior Research Scientist
speech and language processing
Jim Glass
T 253-1640
RM 32-G444

Lead

Spoken Language Systems Group
Designing Information-Rich Embedding Spaces
Unsupervised Learning of Interpretable Representations from Sequential Data
Fact-Checking and Reasoning
Speaker Verification and Diarization
Automatic Speech Recognition
Language and Dialect Identification

Member

Using Computers to Eat Healthier
Low-Power Speech Processing Circuits
Crossing the Vision-Language Boundary
Unsupervised Speech Processing
Arabic Language Processing
Health-related Biomarkers from Speech
Community Question Answering
Understanding Language Representations in Deep Learning Models
Embodied Intelligence Community of Research
speech and language processing
default headshot

Evan Hernandez

Graduate Student
Natural language processing, machine learning, interpretability
default headshot
Natural language processing, machine learning, interpretability
Tommi Jaakkola

PI
Core/Dual

Tommi Jaakkola

Professor
Machine learning, natural language processing, genomics
Tommi Jaakkola
T 253-0440
RM 32-G470

Lead

Machine Learning
Natural Language Processing Group
Aspect-Augmented Adversarial Networks for Domain Adaptation
Learning Strategic Games
Structured Prediction Through Randomization
Interpretability in complex machine learning models

Member

Learning Optimal Interventions
Vertical AI Community of Research
Machine learning, natural language processing, genomics

25 News Results

CSAIL1
Algorithms & Theory Computer Architecture Big Data Cybersecurity Health Care Internet of Things Manufacturing

MIT hosts workshop on theoretical foundations of deep learning

Last week MIT’s Institute for Foundations of Data Science (MIFODS) held an interdisciplinary workshop aimed at tackling the underlying theory behind deep learning. Led by MIT professor Aleksander Madry, the event focused on a number of research discussions at the intersection of math, statistics, and theoretical computer science.

MIT hosts workshop on theoretical foundations of deep learning

bbva1
Algorithms & Theory Security & Cryptography Big Data Cybersecurity Education Entertainment Health Care Internet of Things Manufacturing

Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

This week it was announced that MIT professors and CSAIL principal investigators Shafi Goldwasser, Silvio Micali, Ronald Rivest, and former MIT professor Adi Shamir won this year’s BBVA Foundation Frontiers of Knowledge Awards in the Information and Communication Technologies category for their work in cryptography.

Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

Researchers at MIT and Massachusetts General Hospital have designed a new system that can efficiently search through hundreds of hours of video of laparoscopic surgeries to identify relevant events and visual features.
AI & ML Big Data Health Care

Shrinking data for surgical training

Laparoscopy is a surgical technique in which a fiber-optic camera is inserted into a patient’s abdominal cavity to provide a video feed that guides the surgeon through a minimally invasive procedure. Laparoscopic surgeries can take hours, and the video generated by the camera — the laparoscope — is often recorded. Those recordings contain a wealth of information that could be useful for training both medical providers and computer systems that would aid with surgery, but because reviewing them is so time consuming, they mostly sit idle.

Shrinking data for surgical training

Daniel Jackson was awarded the Outstanding Research Award and Impact Paper Award (photo credit Sharona Jacobs)
Algorithms & Theory AI & ML Big Data Cybersecurity

CSAIL's Daniel Jackson receives two ACM awards

This week the Association for Computer Machinery presented CSAIL principal investigator Daniel Jackson with the 2017 ACM SIGSOFT Outstanding Research Award for his pioneering work in software engineering. (This fall he also received the ACM SIGSOFT Impact Paper Award for his research method for finding bugs in code.)An EECS professor and associate director of CSAIL, Jackson was given the Outstanding Research Award for his “foundational contributions to software modeling, the creation of the modeling language Alloy, and the development of a widely used tool supporting model verification.”

CSAIL's Daniel Jackson receives two ACM awards

Researchers at MIT’s Microsystems Technology Laboratories have built a low-power chip specialized for automatic speech recognition. With power savings of 90 to 99 percent, it could make voice control practical for relatively simple electronic devices.
AI & ML Human-Computer Interaction Energy Internet of Things Manufacturing

Voice control everywhere

The butt of jokes as little as 10 years ago, automatic speech recognition is now on the verge of becoming people’s chief means of interacting with their principal computing devices. In anticipation of the age of voice-controlled electronics, MIT researchers have built a low-power chip specialized for automatic speech recognition. Whereas a cellphone running speech-recognition software might require about 1 watt of power, the new chip requires between 0.2 and 10 milliwatts, depending on the number of words it has to recognize.

Voice control everywhere

MIT researchers have developed a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings.
AI & ML Human-Computer Interaction Big Data

Learning words from pictures

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

Learning words from pictures

System that learns to distinguish words’ phonemes could aid development of speech-processing systems for under-studied languages
AI & ML Human-Computer Interaction Big Data Education Entertainment Internet of Things

Learning spoken language

Every language has its own collection of phonemes, or the basic phonetic units from which spoken words are composed. Depending on how you count, English has somewhere between 35 and 45. Knowing a language’s phonemes can make it much easier for automated systems to learn to interpret speech.In the 2015 volume of Transactions of the Association for Computational Linguistics, CSAIL researchers describe a new machine-learning system that, like several systems before it, can learn to distinguish spoken words. But unlike its predecessors, it can also learn to distinguish lower-level phonetic units, such as syllables and phonemes.

Learning spoken language