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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21 Group Results

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

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

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.
Randall Davis
Fox 1
Jackson image
+4
Stefanie Mueller

Stefanie Mueller

Leads

Stefanie Mueller

Stefanie Mueller

Research Areas

Graphics & Vision
Human-Computer Interaction
Programming Languages & Software Engineering

Impact Areas

Education
Entertainment
Manufacturing

Lead

Stefanie Mueller

Stefanie Mueller

Randall Davis
Fox 1
Jackson image
+4
 
Stefanie Mueller

Stefanie Mueller

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

Community of Research

Theory of Computation Community of Research

The goal of the Theory of Computation CoR is to study the fundamental strengths and limits of computation as well as how these interact with mathematics, computer science, and other disciplines.
Berger
Erik Demaine
Goemans
+20
Daskalakis

Costis Daskalakis

Leads

Daskalakis

Costis Daskalakis

Research Areas

Algorithms & Theory
Computational Biology
Programming Languages & Software Engineering
Security & Cryptography

Impact Areas

Big Data
Cybersecurity

Lead

Daskalakis

Costis Daskalakis

Berger
Erik Demaine
Goemans
+20
 
Daskalakis

Costis Daskalakis

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.
Amarasinghe
Regina Barzilay
Broderick-headshot
+7
Rinard

Martin Rinard

Leads

Rinard

Martin Rinard

Research Areas

Algorithms & Theory
AI & ML
Graphics & Vision
Programming Languages & Software Engineering
Security & Cryptography
Systems & Networking

Impact Areas

Big Data
Cybersecurity
Health Care
Internet of Things
Transportation
Wireless

Lead

Rinard

Martin Rinard

Amarasinghe
Regina Barzilay
Broderick-headshot
+7
 
Rinard

Martin Rinard

27 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

Computational Knitting: Patterning and Shaping for Everyone

Computational Design and Fabrication Group
Knitting is the new 3d printing. It has become popular again with the widespread availability of patterns and templates, together with the maker movements. Lower-cost industrial knitting machines are starting to emerge, but we are still missing the corresponding design tools. Our goal is to fill this gap.
default headshot

Alexandre Kaspar

Leads

default headshot

Alexandre Kaspar

Research Areas

Graphics & Vision
Human-Computer Interaction

Impact Areas

Entertainment
Manufacturing
 
default headshot

Alexandre Kaspar

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

Project

PrivacyML - A Privacy Preserving Framework for Machine Learning

Decentralized Information Group
Cognitive AI Community of Research
Computing & Society Community of Research
We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.
default headshot
default headshot
Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Leads

Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Research Areas

AI & ML
Security & Cryptography

Impact Areas

Big Data
Cybersecurity
Education
Energy
Entertainment
Health Care
Internet of Things
Manufacturing
Transportation
default headshot
default headshot
 
Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Project

Program Performance Prediction with Deep Learning

Programming Systems Group
Commit Group
Vertical AI Community of Research
Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady-state (the throughput) is important for both compiler designers and performance engineers.

However, building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error-prone, and must be performed from scratch for each processor generation.

Ithemal is the first tool that learns to predict the throughput of a set of instructions. It does so more accurately than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, Ithemal has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA).
default headshot
Michael Carbin
Amarasinghe

Leads

Research Areas

Programming Languages & Software Engineering

Impact Areas

Big Data
default headshot
Michael Carbin
Amarasinghe

29 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
Jackson image

PI
Core/Dual

Daniel Jackson

Professor
Software design, programming, systems
Jackson image
T 258-8471
RM 32-G704

Lead

Human-Computer Interaction
Software Design Group
Rethinking Software Design
Gitless: a simple version control system built on top of Git
Déjà Vu: Constructing Apps from Concepts
Security by Design for Cyberphysical Systems
Poirot: Secure Protocol Implementation by Design
An Interlock for Self Driving Cars
Alloy
Wildcard

Member

Espalier
Human-Computer Interaction Community of Research
Systems Community of Research
Wildcard
Software design, programming, systems
Karger

PI
Core/Dual

David Karger

Professor
Helping people manage information
Karger
T 258-6167
RM 32-G592

Lead

Haystack Group
Human-Computer Interaction
Algorithms Group
Mavo: Creating Interactive Data-Driven Web Applications by Authoring HTML
Wikum: Bridging Discussion Systems and Wikis with Collective Summarization
Squadbox: Combating Online Harassment using Friendsourced Moderation
Eyebrowse: Social and Public Web Browsing
Talkademic: Motivating Online Academic Discourse

Member

Human-Computer Interaction Community of Research
Theory of Computation Community of Research
Systems Community of Research
Helping people manage information
default headshot

Henry Lieberman

Research Scientist
Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
default headshot
Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
Rus

PI
Core/Dual

Daniela Rus

Director
CSAIL director, robotics, AI, data science, privacy
Rus
T 258-7567
RM 32/ 32-368/ 374

Lead

Distributed Robotics Laboratory
Social Network Extraction from GPS Datasets with Coresets
Safety Standards for Autonomous Vehicles
Soft Contact Modeling
Practical Secure Computation
Self-Driving Vehicles
Robust Learning for Autonomous Vehicles
Brain-Controlled Robots
Planning in Congestion for Autonomous Vehicles
Socially-Compliant Behavior for Autonomous Driving
Coresets for Machine Learning Algorithms
M-Blocks Modular Robotics
Robot Compiler
Distributed Robot Garden
Route and Motion Planning for Mobility-on-Demand with Rules of the Road

Member

Social Network Extraction from GPS Datasets with Coresets
Task-Driven Co-Optimization Of Articulated Robots
Wearable Blind Navigation
Drones that Drive
Better Models for Ride-Sharing
SoFi - The Soft Robotic Fish
Controlling drones and other robots with gestures
Using Muscle Signals to Lift Objects with Robots
Embodied Intelligence Community of Research
Helping robots learn using demonstrations
CSAIL director, robotics, AI, data science, privacy
Shun

PI
Core/Dual

Julian Shun

Assistant Professor
Parallel computing, applied algorithms, graph processing.
Shun
T 258-0669
RM 32-G736

Lead

Programming Abstractions for Dynamic Graph Analytics
Bridging Theory and Practice in Shared-Memory Parallel Algorithm Design
High-Performance Parallel Clustering

Member

Data Systems Group
Theory of Computation Community of Research
Vertical AI Community of Research
Systems Community of Research
Parallel computing, applied algorithms, graph processing.

22 News Results

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’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