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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8 Group Results

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

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

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

John W. Fisher III

John Fisher

Freeman

William Freeman

Polina Golland

Polina Golland

Grimson

Eric Grimson

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Torralba

Antonio Torralba

Research Areas

AI & ML
Graphics & Vision

Impact Areas

Entertainment
Health Care
Transportation

Lead

Ted Adelson

Ted Adelson

Durand

Fredo Durand

John W. Fisher III

John Fisher

Freeman

William Freeman

Polina Golland

Polina Golland

Grimson

Eric Grimson

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Torralba

Antonio Torralba

12 Project Results

Project

Artificial Social Intelligence for Successful Teams (ASIST)

Computation Structures Group
Computational Cognitive Science Group
Model-Based Embedded and Robotics Systems Group
Our goal is to develop a socially intelligent team coacher agent that helps humans communicate, strategize, and work together efficiently.

Leads

Howard Shrobe

Howard Shrobe

Williams

Brian Williams

Katz

Boris Katz

default headshot

Zhutian Yang

Research Areas

Computer Architecture
Human-Computer Interaction
Robotics

Impact Areas

Health Care
Transportation
Wireless

13 People Results

...

Michael Foshey

Mechanical Engineer/Project Manager
Additive Manufacturing, Robotics, Digital Fabrication
...
RM 32-321
Additive Manufacturing, Robotics, Digital Fabrication
Freeman

PI
Core/Dual

William Freeman

Professor
Vision, machine learning, Bayesian modeling
Freeman
T 253-8828
RM 32-D476

Lead

Vision Group
Computer Graphics Group
Visual Computing Community of Research

Member

Single-Image 3D Interpreter Network
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
Learning the Arrow of Time from Videos
3D Generative Adversarial Networks
Embodied Intelligence Community of Research
Vision, machine learning, Bayesian modeling
Torralba

PI
Core/Dual

Antonio Torralba

Professor
Vision, graphics, perception, machine learning
Torralba
T 324-0900
RM 32-386G

Lead

Vision Group
Understanding Light via Deep Neural Networks
Understandable Deep Networks
Understanding Food images

Member

Predictive Vision
Where Are They Looking?
Single-Image 3D Interpreter Network
VirtualHome: Representing Activities as Programs
Places Database for Scene Recognition
Semantic Scene Understanding through ADE20K dataset
Crossing the Vision-Language Boundary
Center for Deployable Machine Learning (CDML)
Visual Computing Community of Research
Embodied Intelligence Community of Research
Vision, graphics, perception, machine learning

10 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

System directs camera-equipped drones to maintain framing of an aerial shot.
Algorithms & Theory AI & ML Graphics & Vision Robotics Entertainment Transportation

Cinematography on the fly

In recent years, a host of Hollywood blockbusters — including “The Fast and the Furious 7,” “Jurassic World,” and “The Wolf of Wall Street” — have included aerial tracking shots provided by drone helicopters outfitted with cameras. Those shots required separate operators for the drones and the cameras, and careful planning to avoid collisions. But a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable.

Cinematography on the fly