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

Filter options
Filter options

Filter options Close

Role

Media

Research Areas

Impact Areas

Reset Filters

12 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

Cognitive AI Community of Research

This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.
Brewer-headshot
Randall Davis
Kagal-headshot
+6
Howard Shrobe

Howard Shrobe

Leads

Howard Shrobe

Howard Shrobe

Research Areas

Human-Computer Interaction
Robotics
Security & Cryptography
Systems & Networking

Impact Areas

Cybersecurity
Education

Lead

Howard Shrobe

Howard Shrobe

Brewer-headshot
Randall Davis
Kagal-headshot
+6
 
Howard Shrobe

Howard Shrobe

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

8 Project Results

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

18 People Results

Chlipala-headshot

PI
Core/Dual

Adam Chlipala

Associate Professor
Programming languages, formal methods, security & privacy, systems
Chlipala-headshot
T 324-8439
RM 32-G842

Lead

Kami: A Modular Deductive Hardware Verification Platform
Compilation Using Correct-by-Construction Program Synthesis
Programming Languages & Verification
Hemiola: Structural Design and Proof of Cache-Coherence Protocols

Member

Compilation Using Correct-by-Construction Program Synthesis
Programming with spaces
Computation Structures Group
FSCQ: File-system verification
Systems Community of Research
Programming languages, formal methods, security & privacy, systems
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
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
Howard Shrobe

PI
Core/Dual

Howard Shrobe

Principal Research Scientist
Architectures, cybersecurity, web policy
Howard Shrobe
T 253-7877
RM 32-225

Lead

RADMAX
Risk-aware Network Resource Allocation
Automated Attack Tree Generation for Critical Infrastructure
Securing IoT
Cognitive AI Community of Research
Artificial Social Intelligence for Successful Teams (ASIST)
Uhura: Personal Assistant that Manages Risk

Member

Computation Structures Group
Computing & Society Community of Research
Architectures, cybersecurity, web policy

11 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

WaitSuite opens up new possibilities for "micro-learning," leveraging idle moments like when you're waiting to connect to WiFi or refresh your email.
AI & ML Human-Computer Interaction Education Internet of Things

Learn a language while you wait for WiFi

Hyper-connectivity has changed the way we communicate, wait, and productively use our time. Even in a world of 5G wireless and “instant” messaging, there are countless moments throughout the day when we’re waiting for messages, texts, and Snapchats to refresh. But our frustrations with waiting a few extra seconds for our emails to push through doesn’t mean we have to simply stand by.

Learn a language while you wait for WiFi

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

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