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

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

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

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Torralba

Antonio Torralba

Grimson

Eric Grimson

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

Horn

Berthold Horn

oliva-headshot

Aude Oliva

Torralba

Antonio Torralba

Grimson

Eric Grimson

12 Project Results

Project

An Interlock for Self Driving Cars

Software Design Group
Computer-Aided Programming
Vertical AI Community of Research
Human-Computer Interaction Community of Research
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 & ML
Human-Computer Interaction
Programming Languages & Software Engineering
Robotics
Systems & Networking

Impact Areas

Transportation
 
Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

14 People Results

Gregory Falco

Gregory Falco

Research Affiliate
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.
...

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

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
Visual Computing Community of Research
Vision, machine learning, Bayesian modeling
Rus

PI
Core/Dual

Daniela Rus

Director
CSAIL director, robotics, AI, data science, privacy
Rus
T 258-7567
RM 32/ 32-368/ 374

Lead

Distributed Robotics Laboratory
Social Network Extraction from GPS Datasets with Coresets
Safety Standards for Autonomous Vehicles
Soft Contact Modeling
Practical Secure Computation
Self-Driving Vehicles
Robust Learning for Autonomous Vehicles
Brain-Controlled Robots
Planning in Congestion for Autonomous Vehicles
Socially-Compliant Behavior for Autonomous Driving
Coresets for Machine Learning Algorithms
M-Blocks Modular Robotics
Robot Compiler
Distributed Robot Garden
Route and Motion Planning for Mobility-on-Demand with Rules of the Road

Member

Social Network Extraction from GPS Datasets with Coresets
Task-Driven Co-Optimization Of Articulated Robots
Wearable Blind Navigation
Drones that Drive
Better Models for Ride-Sharing
SoFi - The Soft Robotic Fish
Controlling drones and other robots with gestures
Using Muscle Signals to Lift Objects with Robots
Embodied Intelligence Community of Research
Helping robots learn using demonstrations
CSAIL director, robotics, AI, data science, privacy
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

8 News Results

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

Image: Jose-Luis Olivares
Algorithms & Theory AI & ML Systems & Networking Big Data Energy

What better wind-speed prediction can do for the energy industry

When a power company wants to build a new wind farm, it generally hires a consultant to make wind speed measurements at the proposed site for eight to 12 months. Those measurements are correlated with historical data and used to assess the site’s power-generation capacity.This month CSAIL researchers will present a new statistical technique that yields better wind-speed predictions than existing techniques do — even when it uses only three months’ worth of data. That could save power companies time and money, particularly in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly costly.

What better wind-speed prediction can do for the energy industry