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

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Research Areas

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12 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

16 Project Results matching all criteria

Project

MOOC Learner Project: Data science for e-learning

Anyscale Learning for All ALFA
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.
Nhoffman
Una-May O'Reilly

Una-May O'Reilly

Erik Hemberg

Leads

Una-May O'Reilly

Una-May O'Reilly

Erik Hemberg

Research Areas

AI & Machine Learning

Impact Areas

Big Data
Education
Nhoffman
 
Una-May O'Reilly

Una-May O'Reilly

Erik Hemberg

Project

Unsupervised Learning of Interpretable Representations from Sequential Data

Spoken Language Systems Group
Generation of sequential data involves multiple factors operating at different temporal scales. Take natural speech for example, the speaker identity tends to be consistent within an utterance, while the phonetic content changes from frame to frame. By explicitly modeling such hierarchical generative process under a probabilistic framework, we proposed a model that learns to factorizes sequence-level factors and sub-sequence-level factors into different sets of representations without any supervision.
weining_hsu

Wei-Ning Hsu

Jim Glass

Jim Glass

Leads

weining_hsu

Wei-Ning Hsu

Jim Glass

Jim Glass

Research Areas

AI & Machine Learning

Impact Areas

Big Data
 
weining_hsu

Wei-Ning Hsu

Jim Glass

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

Unsupervised Speech Processing

Spoken Language Systems Group
All humans process vast quantities of unannotated speech and manage to learn phonetic inventories, word boundaries, etc., and can use these abilities to acquire new word. Why can't ASR technology have similar capabilities? Our goal in this research project is to build speech technology using unannotated speech corpora.
Jim Glass

Leads

image

Jennifer Drexler

Yu-An Chung (Andy)

Yu-An Chung

MIT CSAIL Spoken Language Systems PhD student

Sameer Khurana

Harwath-headshot

David Harwath

Research Areas

AI & Machine Learning
Human-Computer Interaction

Impact Areas

Big Data
Education
Health Care
Jim Glass

Project

PrivacyML - A Privacy Preserving Framework for Machine Learning

Decentralized Information Group
We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.
Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Leads

Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Research Areas

AI & Machine Learning
Security & Cryptography

Impact Areas

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

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

48 People Results matching all criteria

Gregory Falco

Gregory Falco

Postdoctoral Associate
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

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
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

Aspect-Augmented Adversarial Networks for Domain Adaptation
Learning Optimal Interventions
Machine learning, natural language processing, genomics

10 News Results matching all criteria

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 & Machine Learning 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 & Machine Learning 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

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

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

12 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

16 Project Results

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

MOOC Learner Project: Data science for e-learning

Anyscale Learning for All ALFA
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.
Nhoffman
Una-May O'Reilly

Una-May O'Reilly

Erik Hemberg

Leads

Una-May O'Reilly

Una-May O'Reilly

Erik Hemberg

Research Areas

AI & Machine Learning

Impact Areas

Big Data
Education
Nhoffman
 
Una-May O'Reilly

Una-May O'Reilly

Erik Hemberg

Project

PrivacyML - A Privacy Preserving Framework for Machine Learning

Decentralized Information Group
We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.
Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Leads

Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Research Areas

AI & Machine Learning
Security & Cryptography

Impact Areas

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

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Project

Unsupervised Learning of Interpretable Representations from Sequential Data

Spoken Language Systems Group
Generation of sequential data involves multiple factors operating at different temporal scales. Take natural speech for example, the speaker identity tends to be consistent within an utterance, while the phonetic content changes from frame to frame. By explicitly modeling such hierarchical generative process under a probabilistic framework, we proposed a model that learns to factorizes sequence-level factors and sub-sequence-level factors into different sets of representations without any supervision.
weining_hsu

Wei-Ning Hsu

Jim Glass

Jim Glass

Leads

weining_hsu

Wei-Ning Hsu

Jim Glass

Jim Glass

Research Areas

AI & Machine Learning

Impact Areas

Big Data
 
weining_hsu

Wei-Ning Hsu

Jim Glass

Jim Glass

Project

Unsupervised Speech Processing

Spoken Language Systems Group
All humans process vast quantities of unannotated speech and manage to learn phonetic inventories, word boundaries, etc., and can use these abilities to acquire new word. Why can't ASR technology have similar capabilities? Our goal in this research project is to build speech technology using unannotated speech corpora.
Jim Glass

Leads

image

Jennifer Drexler

Yu-An Chung (Andy)

Yu-An Chung

MIT CSAIL Spoken Language Systems PhD student

Sameer Khurana

Harwath-headshot

David Harwath

Research Areas

AI & Machine Learning
Human-Computer Interaction

Impact Areas

Big Data
Education
Health Care
Jim Glass

48 People Results

Gregory Falco

Gregory Falco

Postdoctoral Associate
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

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
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

Aspect-Augmented Adversarial Networks for Domain Adaptation
Learning Optimal Interventions
Machine learning, natural language processing, genomics

10 News Results

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 & Machine Learning 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 & Machine Learning 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

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

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