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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24 Group Results matching all criteria

Research Group

Vision Group

Our researchers create state-of-the-art systems to better recognize objects, people, scenes, behaviors and more, with applications in health-care, gaming, tagging systems and more.

Leads

Ted Adelson

Ted Adelson

Durand

Fredo Durand

Fisher

John Fisher

Freeman

William Freeman

Polina Golland

Polina Golland

Grimson

Eric Grimson

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Ruth Rosenholtz

Ruth Rosenholtz

Torralba

Antonio Torralba

Research Areas

AI & Machine Learning
Graphics & Vision

Impact Areas

Entertainment
Health Care
Transportation

Lead

Ted Adelson

Ted Adelson

Durand

Fredo Durand

Fisher

John Fisher

Freeman

William Freeman

Polina Golland

Polina Golland

Grimson

Eric Grimson

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Ruth Rosenholtz

Ruth Rosenholtz

Torralba

Antonio Torralba

60 Project Results matching all criteria

Project

Aspect-Augmented Adversarial Networks for Domain Adaptation

Natural Language Processing Group
We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.
Regina Barzilay
Tommi Jaakkola
Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

Leads

Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

Research Areas

Algorithms & Theory
AI & Machine Learning
Systems & Networking
Regina Barzilay
Tommi Jaakkola
 
Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

Project

Toward Using Virtual Identities in Computer Science Learning for Broadening Participation

Imagination, Computation, and Expression Laboratory (ICE Lab)
Our research seeks to discover best practices for using avatars to enhance performance, engagement, and STEM identity development for diverse public middle and high school computer science students. As sites of our research we run workshops in which students learn computer science in fun, relevant ways, and develop self-images as computer scientists.

Sneha Veeragoudar Harrell

Aziria Rodriguez

+1
Harrell

D. Fox Harrell

Leads

Harrell

D. Fox Harrell

Research Areas

AI & Machine Learning

Impact Areas

Education
Entertainment

Sneha Veeragoudar Harrell

Aziria Rodriguez

+1
 
Harrell

D. Fox Harrell

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

39 News Results matching all criteria

F. Harrell
AI & Machine Learning Programming Languages & Software Engineering Education Entertainment

3Q: D. Fox Harrell on his video game for the #MeToo era

The Imagination, Computation, and Expression Laboratory at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has released a new video game called Grayscale, which is designed to sensitize players to problems of sexism, sexual harassment, and sexual assault in the workplace.

3Q: D. Fox Harrell on his video game for the #MeToo era

A new system from MIT's Computer Science and Artificial Intelligence Laboratory works by analyzing light at the edge of walls, which is impacted by the reflections of objects around the corner from the camera.
AI & Machine Learning Graphics & Vision Transportation

An algorithm for your blind spot

Light lets us see the things that surround us, but what if we could also use it to see things hidden around corners? It sounds like science fiction, but that’s the idea behind a new algorithm out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) — and its discovery has implications for everything from emergency response to self-driving cars.

An algorithm for your blind spot

Machine learning approach could aid the design of industrial processes for drug manufacturing.
AI & Machine Learning Computational Biology Health Care

Computer system predicts products of chemical reactions

When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it. There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.

Computer system predicts products of chemical reactions

24 Group Results

Research Group

Vision Group

Our researchers create state-of-the-art systems to better recognize objects, people, scenes, behaviors and more, with applications in health-care, gaming, tagging systems and more.

Leads

Ted Adelson

Ted Adelson

Durand

Fredo Durand

Fisher

John Fisher

Freeman

William Freeman

Polina Golland

Polina Golland

Grimson

Eric Grimson

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Ruth Rosenholtz

Ruth Rosenholtz

Torralba

Antonio Torralba

Research Areas

AI & Machine Learning
Graphics & Vision

Impact Areas

Entertainment
Health Care
Transportation

Lead

Ted Adelson

Ted Adelson

Durand

Fredo Durand

Fisher

John Fisher

Freeman

William Freeman

Polina Golland

Polina Golland

Grimson

Eric Grimson

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Ruth Rosenholtz

Ruth Rosenholtz

Torralba

Antonio Torralba

60 Project Results

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
alhanai-headshot
Jim Glass

Leads

cannot load image

Ramy Baly

Belinkov-headshot

Yonatan Belinkov

Mitra Mohtarami

Mitra Mohtarami

Research Areas

AI & Machine Learning
Human-Computer Interaction
Shon
alhanai-headshot
Jim Glass

Project

Aspect-Augmented Adversarial Networks for Domain Adaptation

Natural Language Processing Group
We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.
Regina Barzilay
Tommi Jaakkola
Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

Leads

Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

Research Areas

Algorithms & Theory
AI & Machine Learning
Systems & Networking
Regina Barzilay
Tommi Jaakkola
 
Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

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

alhanai-headshot

Tuka Al Hanai

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

CliNER: Clinical Concept Extraction

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

Card Image of Tristan Naumann

Tristan Naumann

Elena Sergeeva

Elena Sergeeva

Szolovits

Peter Szolovits

Anna Rumshisky

Research Areas

AI & Machine Learning

Impact Areas

Energy

39 News Results