Research

  • Research Areas
  • Impact Areas

Algorithms & Theory

Foundational work that includes complexity, parallel computing and game theory

AI & Machine Learning

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

Opportunities Available

Research Areas

Impact Areas

Reset Filters

11 Group Results matching all criteria

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.
+3

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 & Machine Learning
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

+3

41 Project Results matching all criteria

Project

Culturally-Grounded Virtual Identities: A Case Study in a Qatari Context

Imagination, Computation, and Expression Laboratory (ICE Lab)
Mixed-methods qualitative (interviews and coding) and computational (AI) approach to understanding relationships between social identities, cultural values, and virtual identity technologies (e.g., online profiles and avatars).
Fox 1

D. Fox Harrell

Leads

Fox 1

D. Fox Harrell

Research Areas

Human-Computer Interaction

Impact Areas

Education
Entertainment
 
Fox 1

D. Fox Harrell

Project

Mavo: Creating Interactive Data-Driven Web Applications by Authoring HTML

Haystack Group
Theory of Computation Group
Mavo is a language that lets anyone turn a static HTML document into a fully functioning reactive web application with data presentation, editing, storage and lightweight computation, all without writing a single line of Javascript or other programming code.
Karger

David Karger

Lea Verou

Leads

Karger

David Karger

Lea Verou

Research Areas

Human-Computer Interaction
Programming Languages & Software Engineering
 
Karger

David Karger

Lea Verou

Project

Efficient Communication for Human-Machine Teams

Interactive Robotics Group
Our goal is to develop collaborative agents (software or robots) that can efficiently communicate with their human teammates. Key threads involve designing algorithms for inferring human behavior and for decision-making under uncertainty.

X. Jessie Yang

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Leads

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Research Areas

Human-Computer Interaction
Robotics

Impact Areas

Manufacturing

X. Jessie Yang

 

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Project

Provoking Critical Reflection on Gender Discrimination with Chimeria: Grayscale

Imagination, Computation, and Expression Laboratory (ICE Lab)
The computer as a medium offers a unique expressive palette for storytellers. With it, we can build and convey models of crucial, moving issues in our world. As a step toward this aim as it relates to sexism, we present our interactive narrative called Grayscale. The experience is intended to provoke players to reflect critically on sexism in the workplace, both overt & hostile and more subtle.
Fox 1

D. Fox Harrell

Leads

Fox 1

D. Fox Harrell

Research Areas

Human-Computer Interaction
 
Fox 1

D. Fox Harrell

28 People Results matching all criteria

Joseph DelPreto

Joseph DelPreto

Graduate Student
Works on human-robot interaction using muscle and brain signals.
Joseph DelPreto
RM 32-376A

Lead

Supervising Robots with Muscle and Brain Signals
Brain-Controlled Robots
Distributed Robot Garden
Robot Compiler
SoFi - The Soft Robotic Fish
Soft Robotic Hand
Using Muscle Signals to Lift Objects with Robots

Member

Distributed Robotics Laboratory
Using Muscle Signals to Lift Objects with Robots
Works on human-robot interaction using muscle and brain signals.
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

Wait-learning: Intelligent Systems for Making Productive Use of Wait Time
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
speech and language processing

Henry Lieberman

Research Scientist
Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
Photo of Jamie Macbeth

Jamie Macbeth

Research Affiliate
In-Depth Natural Language Understanding, Human-Computer Interaction
Photo of Jamie Macbeth
In-Depth Natural Language Understanding, Human-Computer Interaction

9 News Results matching all criteria

PhD student Danielle Olson believes we can make digital information-sharing more natural and interpersonal, by creating immersive media to better understand each other’s feelings and backgrounds.
AI & Machine Learning Human-Computer Interaction Entertainment

Danielle Olson: Building empathy through computer science and art

Communicating through computers has become an extension of our daily reality. But as speaking via screens has become commonplace, our exchanges are losing inflection, body language, and empathy. Danielle Olson ’14, a first-year PhD student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), believes we can make digital information-sharing more natural and interpersonal, by creating immersive media to better understand each other’s feelings and backgrounds.

Danielle Olson: Building empathy through computer science and art

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 & Machine Learning 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 & Machine Learning 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

A prototype of a new speech-controlled nutrition-logging system allows users to verbally describe the contents of a meal. The system then parses the description and automatically retrieves the pertinent nutritional data.
AI & Machine Learning Human-Computer Interaction Health Care

Voice-controlled calorie counter

For people struggling with obesity, logging calorie counts and other nutritional information at every meal is a proven way to lose weight. The technique does require consistency and accuracy, however, and when it fails, it’s usually because people don't have the time to find and record all the information they need.A few years ago, a team of nutritionists from Tufts University who had been experimenting with mobile-phone apps for recording caloric intake approached members of the Spoken Language Systems Group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), with the idea of a spoken-language application that would make meal logging even easier.

Voice-controlled calorie counter

System that learns to distinguish words’ phonemes could aid development of speech-processing systems for under-studied languages
AI & Machine Learning 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

11 Group Results

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.
+3

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 & Machine Learning
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

+3

41 Project Results

Project

Alloy

Software Design Group
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. Now one of the main challenges is to make the system more usable and understandable for the user.
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

An Interlock for Self Driving Cars

Software Design Group
Computer-Aided Programming
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.
Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

Leads

Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

Research Areas

AI & Machine Learning
Human-Computer Interaction
Programming Languages & Software Engineering
Robotics
Systems & Networking

Impact Areas

Transportation
 
Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

Project

Arabic Language Processing

Spoken Language Systems Group
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.
Shon
Jim Glass

Leads

cannot load image

Ramy Baly

headshot

Yonatan Belinkov

Mitra Mohtarami

Mitra Mohtarami

Research Areas

AI & Machine Learning
Human-Computer Interaction
Shon
Jim Glass

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.

Leads

Hao Tang

Hao Tang

weining_hsu

Wei-Ning Hsu

Harwath-headshot

David Harwath

MIT CSAIL Spoken Language Systems PhD student

Sameer Khurana

Di-Chia Chueh Profile Image

Di-Chia Chueh

Jim Glass

Jim Glass

Research Areas

AI & Machine Learning
Human-Computer Interaction

Impact Areas

Big Data
Education
Entertainment
Health Care
Internet of Things

Project

Computational Knitting: Patterning and Shaping for Everyone

Computational 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.

Alexandre Kaspar

Leads

Alexandre Kaspar

Research Areas

Graphics & Vision
Human-Computer Interaction

Impact Areas

Entertainment
Manufacturing
 

Alexandre Kaspar

Project

Culturally-Grounded Virtual Identities: A Case Study in a Qatari Context

Imagination, Computation, and Expression Laboratory (ICE Lab)
Mixed-methods qualitative (interviews and coding) and computational (AI) approach to understanding relationships between social identities, cultural values, and virtual identity technologies (e.g., online profiles and avatars).
Fox 1

D. Fox Harrell

Leads

Fox 1

D. Fox Harrell

Research Areas

Human-Computer Interaction

Impact Areas

Education
Entertainment
 
Fox 1

D. Fox Harrell

Project

Déjà Vu: Constructing Apps from Concepts

Software Design Group
Déjà Vu is a new platform for end-user development of apps with rich functionality. It features a novel theory of modularity for binding concepts; an extensive library of reusable concepts; and a WYSIWYG tool for specifying bindings and customizing visual layout
Photo of Santiago

Santiago Perez De Rosso

Jackson image

Daniel Jackson

Leads

Photo of Santiago

Santiago Perez De Rosso

Jackson image

Daniel Jackson

Research Areas

Human-Computer Interaction
Programming Languages & Software Engineering
 
Photo of Santiago

Santiago Perez De Rosso

Jackson image

Daniel Jackson

Project

Efficient Communication for Human-Machine Teams

Interactive Robotics Group
Our goal is to develop collaborative agents (software or robots) that can efficiently communicate with their human teammates. Key threads involve designing algorithms for inferring human behavior and for decision-making under uncertainty.

X. Jessie Yang

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Leads

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Research Areas

Human-Computer Interaction
Robotics

Impact Areas

Manufacturing

X. Jessie Yang

 

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Project

Mavo: Creating Interactive Data-Driven Web Applications by Authoring HTML

Haystack Group
Theory of Computation Group
Mavo is a language that lets anyone turn a static HTML document into a fully functioning reactive web application with data presentation, editing, storage and lightweight computation, all without writing a single line of Javascript or other programming code.
Karger

David Karger

Lea Verou

Leads

Karger

David Karger

Lea Verou

Research Areas

Human-Computer Interaction
Programming Languages & Software Engineering
 
Karger

David Karger

Lea Verou

28 People Results

Joseph DelPreto

Joseph DelPreto

Graduate Student
Works on human-robot interaction using muscle and brain signals.
Joseph DelPreto
RM 32-376A

Lead

Supervising Robots with Muscle and Brain Signals
Brain-Controlled Robots
Distributed Robot Garden
Robot Compiler
SoFi - The Soft Robotic Fish
Soft Robotic Hand
Using Muscle Signals to Lift Objects with Robots

Member

Distributed Robotics Laboratory
Using Muscle Signals to Lift Objects with Robots
Works on human-robot interaction using muscle and brain signals.
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

Wait-learning: Intelligent Systems for Making Productive Use of Wait Time
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
speech and language processing

Henry Lieberman

Research Scientist
Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
Photo of Jamie Macbeth

Jamie Macbeth

Research Affiliate
In-Depth Natural Language Understanding, Human-Computer Interaction
Photo of Jamie Macbeth
In-Depth Natural Language Understanding, Human-Computer Interaction

9 News Results

PhD student Danielle Olson believes we can make digital information-sharing more natural and interpersonal, by creating immersive media to better understand each other’s feelings and backgrounds.
AI & Machine Learning Human-Computer Interaction Entertainment

Danielle Olson: Building empathy through computer science and art

Communicating through computers has become an extension of our daily reality. But as speaking via screens has become commonplace, our exchanges are losing inflection, body language, and empathy. Danielle Olson ’14, a first-year PhD student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), believes we can make digital information-sharing more natural and interpersonal, by creating immersive media to better understand each other’s feelings and backgrounds.

Danielle Olson: Building empathy through computer science and art

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 & Machine Learning 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 & Machine Learning 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

A prototype of a new speech-controlled nutrition-logging system allows users to verbally describe the contents of a meal. The system then parses the description and automatically retrieves the pertinent nutritional data.
AI & Machine Learning Human-Computer Interaction Health Care

Voice-controlled calorie counter

For people struggling with obesity, logging calorie counts and other nutritional information at every meal is a proven way to lose weight. The technique does require consistency and accuracy, however, and when it fails, it’s usually because people don't have the time to find and record all the information they need.A few years ago, a team of nutritionists from Tufts University who had been experimenting with mobile-phone apps for recording caloric intake approached members of the Spoken Language Systems Group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), with the idea of a spoken-language application that would make meal logging even easier.

Voice-controlled calorie counter

System that learns to distinguish words’ phonemes could aid development of speech-processing systems for under-studied languages
AI & Machine Learning 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