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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1 News Results matching all criteria

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

16 Group Results

Research Group

Algorithms Group

We devise new mathematical tools to tackle the increasing difficulty and importance of problems we pose to computers.

Leads

Daskalakis

Costis Daskalakis

Erik Demaine

Erik Demaine

Goemans

Michael Goemans

Indyk

Piotr Indyk

Karger

David Karger

Jonathan Kelner

Jonathan Kelner

Leighton

Tom Leighton

Professor Charles E. Leiserson

Charles E. Leiserson

Madry-headshot

Aleksander Madry

Moitra-headshot

Ankur Moitra

Rubinfeld

Ronitt Rubinfeld

shavit-headshot

Nir Shavit

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

Research Areas

Algorithms & Theory

Impact Areas

Big Data
Cybersecurity
Energy
Wireless

Lead

Daskalakis

Costis Daskalakis

Erik Demaine

Erik Demaine

Goemans

Michael Goemans

Indyk

Piotr Indyk

Karger

David Karger

Jonathan Kelner

Jonathan Kelner

Leighton

Tom Leighton

Professor Charles E. Leiserson

Charles E. Leiserson

Madry-headshot

Aleksander Madry

Moitra-headshot

Ankur Moitra

Rubinfeld

Ronitt Rubinfeld

shavit-headshot

Nir Shavit

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

Research Center

Center for Deployable Machine Learning (CDML)

The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.
Daskalakis
Moitra-headshot
Rinard
+5
Madry-headshot

Aleksander Madry

Leads

Madry-headshot

Aleksander Madry

Research Areas

Algorithms & Theory
AI & ML
Graphics & Vision
Human-Computer Interaction
Programming Languages & Software Engineering
Robotics
Security & Cryptography

Impact Areas

Big Data
Cybersecurity

Lead

Madry-headshot

Aleksander Madry

Daskalakis
Moitra-headshot
Rinard
+5
 
Madry-headshot

Aleksander Madry

Research Group

Complexity Theory Group

Our interests span quantum complexity theory, barriers to solving P versus NP, theoretical computer science with a focus on probabilistically checkable proofs (PCP), pseudo-randomness, coding theory, and algorithms.

Leads

Goldwasser

Shafi Goldwasser

Rubinfeld

Ronitt Rubinfeld

Sipser

Michael Sipser

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

Research Areas

Algorithms & Theory

Impact Areas

Big Data
Cybersecurity

Lead

Goldwasser

Shafi Goldwasser

Rubinfeld

Ronitt Rubinfeld

Sipser

Michael Sipser

RWilliams-headshot

Ryan Williams

VWilliams-headshot

Virginia Vassilevska Williams

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

Theory of Computation Community of Research

The goal of the Theory of Computation CoR is to study the fundamental strengths and limits of computation as well as how these interact with mathematics, computer science, and other disciplines.
Berger
Erik Demaine
Goemans
+20
Daskalakis

Costis Daskalakis

Leads

Daskalakis

Costis Daskalakis

Research Areas

Algorithms & Theory
Computational Biology
Programming Languages & Software Engineering
Security & Cryptography

Impact Areas

Big Data
Cybersecurity

Lead

Daskalakis

Costis Daskalakis

Berger
Erik Demaine
Goemans
+20
 
Daskalakis

Costis Daskalakis

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

21 Project Results

Project

Active Learning of Models for Planning

Learning and Intelligent Systems
Machine Learning
We aim to develop a systematic framework for robots to build models of the world and to use these to make effective and safe choices of actions to take in complex scenarios.
Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Leads

Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Research Areas

Algorithms & Theory
AI & ML
Human-Computer Interaction
Robotics

Impact Areas

Big Data
Education
Entertainment
 
Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Project

Determining Wikipedia's Influence on Science

Supertech Research Group
Wikipedia is one of the most widely accessed encyclopedia sites in the world, including by scientists. Our project aims to investigate just how far Wikipedia’s influence goes in shaping science.
Neil Thompson Headshot

Neil Thompson

Professor Charles E. Leiserson

Charles E. Leiserson

Leads

Neil Thompson Headshot

Neil Thompson

Professor Charles E. Leiserson

Charles E. Leiserson

Research Areas

Algorithms & Theory
Human-Computer Interaction
Programming Languages & Software Engineering

Impact Areas

Big Data
Education
 
Neil Thompson Headshot

Neil Thompson

Professor Charles E. Leiserson

Charles E. Leiserson

27 People Results

Xuhao Chen

Xuhao Chen

Postdoctoral Associate
Parallel Computer Architecture, Data Mining, Machine Learning
Xuhao Chen
Parallel Computer Architecture, Data Mining, Machine Learning
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

Learning Optimal Interventions
Vertical AI Community of Research
Machine learning, natural language processing, genomics
Karger

PI
Core/Dual

David Karger

Professor
Helping people manage information
Karger
T 258-6167
RM 32-G592

Lead

Haystack Group
Human-Computer Interaction
Algorithms Group
Mavo: Creating Interactive Data-Driven Web Applications by Authoring HTML
Wikum: Bridging Discussion Systems and Wikis with Collective Summarization
Squadbox: Combating Online Harassment using Friendsourced Moderation
Eyebrowse: Social and Public Web Browsing
Talkademic: Motivating Online Academic Discourse

Member

Human-Computer Interaction Community of Research
Theory of Computation Community of Research
Systems Community of Research
Helping people manage information
Shun

PI
Core/Dual

Julian Shun

Assistant Professor
Parallel computing, applied algorithms, graph processing.
Shun
T 258-0669
RM 32-G736

Lead

Programming Abstractions for Dynamic Graph Analytics
Bridging Theory and Practice in Shared-Memory Parallel Algorithm Design
High-Performance Parallel Clustering

Member

Data Systems Group
Theory of Computation Community of Research
Vertical AI Community of Research
Systems Community of Research
Parallel computing, applied algorithms, graph processing.
solomon-headshot

PI
Core/Dual

Justin Solomon

Assistant Professor
Geometry, graphics, vision, machine learning, optimization.
solomon-headshot
RM 32-D460

Lead

Computer Graphics Group
Geometric Data Processing Group
Geometry in Large-Scale Machine Learning
Political Geometry: Establishing Fair Mathematical Standards for Political Redistricting
Sensible Deep Learning for 3D Data
Optimal transport for statistics and machine learning
Geometry and topology for scientific computing and shape analysis

Member

Visual Computing Community of Research
Geometry, graphics, vision, machine learning, optimization.
Russ Tedrake

PI
Core/Dual

Russ Tedrake

Professor
Robotics, machine learning, motion planning and control
Russ Tedrake
T 253-1778
RM 32-380B

Lead

Robot Locomotion Group
Localizing External Contact Using Proprioceptive Sensors
Tactile and Physical Perception
Soft Contact Modeling
Self-Driving Vehicles

Member

Tactile and Physical Perception
Task-Driven Co-Optimization Of Articulated Robots
Center for Deployable Machine Learning (CDML)
Embodied Intelligence Community of Research
Robotics, machine learning, motion planning and control

20 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

bbva1
Algorithms & Theory Security & Cryptography Big Data Cybersecurity Education Entertainment Health Care Internet of Things Manufacturing

Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

This week it was announced that MIT professors and CSAIL principal investigators Shafi Goldwasser, Silvio Micali, Ronald Rivest, and former MIT professor Adi Shamir won this year’s BBVA Foundation Frontiers of Knowledge Awards in the Information and Communication Technologies category for their work in cryptography.

Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

Daniel Jackson was awarded the Outstanding Research Award and Impact Paper Award (photo credit Sharona Jacobs)
Algorithms & Theory AI & ML 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

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