Research

Impact Areas

Lead

Costis Daskalakis

Erik Demaine

Michael Goemans

Piotr Indyk

David Karger

Jonathan Kelner

Tom Leighton

Charles E. Leiserson

Aleksander Madry

Ankur Moitra

Ronitt Rubinfeld

Nir Shavit

Ryan Williams

Virginia Vassilevska Williams

Research Group

Applied Computing Group

We design software for high performance computing, develop algorithms for numerical linear algebra, and research random matrix theory and its applications.

Leads

Research Areas

Impact Areas

Research Group

Broderick Lab

We work in the areas of statistics and machine learning.

Tamara Broderick

Leads

Tamara Broderick

Research Areas

Impact Areas

Lead

Tamara Broderick

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.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Lead

Aleksander Madry

Research Group

Clinical Decision-Making Group

We focus on furthering the application of technology and artificial intelligence in medicine and health-care.

+11

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Lead

Peter Szolovits

+11

Peter Szolovits

Research 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

Shafi Goldwasser

Ronitt Rubinfeld

Michael Sipser

Ryan Williams

Virginia Vassilevska Williams

Research Areas

Impact Areas

Lead

Shafi Goldwasser

Ronitt Rubinfeld

Michael Sipser

Ryan Williams

Virginia Vassilevska Williams

Research Group

Computation and Biology

Our lab focuses on designing algorithms to gain biological insights from advances in automated data collection and the subsequent large data sets drawn from them.

Bonnie Berger

Leads

Bonnie Berger

Research Areas

Impact Areas

Lead

Bonnie Berger

Research Group

Computational Biology Group

We seek to understand the mechanistic basis of human disease, using a combination of computational and experimental techniques.

Manolis Kellis

Leads

Manolis Kellis

Research Areas

Impact Areas

Lead

Manolis Kellis

Research Group

Computational Connectomics Group

Our group’s goal is to create, based on such microscopic connectivity and functional data, new mathematical models explaining how neural tissue computes.

Nir Shavit

Leads

Nir Shavit

Research Areas

Impact Areas

Lead

Nir Shavit

Research Group

Computational Perception & Cognition

We combine methods from computer science, neuroscience and cognitive science to explain and model how perception and cognition are realized in human and machine.

Aude Oliva

Leads

Aude Oliva

Research Areas

Impact Areas

Lead

Aude Oliva

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.

Daniel Weitzner

Leads

Daniel Weitzner

Research Areas

Impact Areas

Lead

Daniel Weitzner

Research Group

Cryptography and Information Security Group

We seek to develop techniques for securing tomorrow's global information infrastructure by exploring theoretical foundations, near-term practical applications, and long-range speculative research.

Leads

Shafi Goldwasser

Silvio Micali

Ronald Rivest

Vinod Vaikuntanathan

Butler Lampson

Research Areas

Impact Areas

Lead

Shafi Goldwasser

Silvio Micali

Ronald Rivest

Vinod Vaikuntanathan

Butler Lampson

Research Group

Decentralized Information Group

We are investigating decentralized technologies that affect social change.

Lalana Kagal

Tim Berners-Lee

Leads

Lalana Kagal

Tim Berners-Lee

Research Areas

Impact Areas

Lead

Lalana Kagal

Tim Berners-Lee

Lalana Kagal

Tim Berners-Lee

Research Group

Geometric Data Processing Group

Our group studies geometric problems in computer graphics, computer vision, machine learning, optimization, and other disciplines.

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Lead

Justin Solomon

Research Group

Haystack Group

We are an interdisciplinary group of researchers blending approaches from human-computer interaction, social computing, databases, information management, and databases.

Lea Verou

David Karger

Leads

David Karger

Research Areas

Impact Areas

Internet of Things

Lead

David Karger

Lea Verou

David Karger

Research Group

Quantum Information Science Group

Our research interests center around the capabilities and limits of quantum computers, and computational complexity theory more generally.

Peter Shor

Leads

Peter Shor

Research Areas

Impact Areas

Energy

Lead

Peter Shor

Research Group

Supertech Research Group

We investigate the technologies that support scalable high-performance computing, including hardware, software, and theory.

Charles E. Leiserson

Neil Thompson

Leads

Charles E. Leiserson

Neil Thompson

Research Areas

Computer Architecture

Impact Areas

Lead

Charles E. Leiserson

Neil Thompson

Charles E. Leiserson

Neil Thompson

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.

+20

Costis Daskalakis

Leads

Costis Daskalakis

Research Areas

Impact Areas

Lead

Costis Daskalakis

+20

Costis Daskalakis

Research Group

Theory of Distributed Systems Group

We work on a wide range of problems in distributed computing theory. We study algorithms and lower bounds for typical problems that arise in distributed systems---like resource allocation, implementing shared memory abstractions, and reliable communication.

Nancy Lynch

Leads

Nancy Lynch

Research Areas

Impact Areas

Lead

Nancy Lynch

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.

Martin Rinard

Leads

Martin Rinard

Research Areas

Learning and Intelligent Systems

Impact Areas

Lead

Martin Rinard

21 Project Results

Project

Active Learning of Models for Planning

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.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Entertainment

Leslie Kaelbling

Tomas Lozano-Perez

Project

Algorithmic Aspects of Performance Engineering

Supertech Research Group

The project concerns algorithmic solutions for writing fast codes.

Leads

Research Areas

Impact Areas

Project

Bayesian Optimization for Global Optimization of Expensive Black-box Functions

Learning and Intelligent Systems

We study the fundamentals of Bayesian optimization and develop efficient Bayesian optimization methods for global optimization of expensive black-box functions originated from a range of different applications.

Leads

Research Areas

Impact Areas

Project

Better Models for Ride-Sharing

Traffic is not just a nuisance for drivers: It’s also a public health hazard and bad news for the economy.

Leads

Research Areas

Impact Areas

Energy

Transportation

Project

Bridging Theory and Practice in Shared-Memory Parallel Algorithm Design

This project aims to design parallel algorithms for shared-memory machines that are efficient both in theory and also in practice.

Julian Shun

Leads

Julian Shun

Research Areas

Impact Areas

Julian Shun

Project

Coresets for Machine Learning Algorithms

Our goal is to design novel data compression techniques to accelerate popular machine learning algorithms in Big Data and streaming settings.

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Daniela Rus

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

Charles E. Leiserson

Leads

Neil Thompson

Charles E. Leiserson

Research Areas

Impact Areas

Learning and Intelligent Systems

Neil Thompson

Charles E. Leiserson

Project

Deterministic Algorithms for Robotic Task and Motion Planning

Our goal is to investigate deterministic algorithms for robotic task and motion planning.

Leads

Research Areas

Impact Areas

Transportation

Project

Distributed Co-prime Sampling Algorithms

To further parallelize co-prime sampling based sparse sensing, we introduce Diophantine Equation in different algebraic structures to build generalized lattice arrays.
With strong relationship to generalized Chinese Remainder Theorem, the geometry properties in the remainder code space, a special lattice space, are explored.

Hanshen Xiao

Leads

Hanshen Xiao

Research Areas

Impact Areas

Hanshen Xiao

Project

Diversity-inducing Probability Measures

We aim to understand theory and applications of diversity-inducing probabilities (and, more generally, "negative dependence") in machine learning, and develop fast algorithms based on their mathematical properties.

Stefanie Jegelka

Suvrit Sra

Leads

Stefanie Jegelka

Suvrit Sra

Research Areas

Impact Areas

Stefanie Jegelka

Suvrit Sra

Project

Geometry and topology for scientific computing and shape analysis

Computer Graphics Group

Developing state-of-the-art tools that process 3D surfaces and volumes

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

Project

Helping robots learn using demonstrations

Help robots learn faster by providing demonstrations when they need help

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Impact Areas

Joseph DelPreto

Project

High-Performance Parallel Clustering

We are designing new parallel algorithms, optimizations, and frameworks for clustering large-scale graph and geometric data.

Julian Shun

Leads

Julian Shun

Research Areas

Impact Areas

Julian Shun

Project

Optimal transport for statistics and machine learning

Linking probability with geometry to improve the theory and practice of machine learning

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

Project

Political Geometry: Establishing Fair Mathematical Standards for Political Redistricting

Gerrymandering is a direct threat to our democracy, undermining founding principles like equal protection under the law and eroding public confidence in elections.

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

Project

Privacy-Preserving Decentralized Optimization

To enable privacy preservation in decentralized optimization, differential privacy is the most commonly used approach. However, under such scenario, the trade-off between accuracy (even efficiency) and privacy is inevitable. In this project, distributed numerical optimization scheme incorporated with lightweight cryptographic information sharing are explored. The affect on the convergence rate from network topology is considered.

Hanshen Xiao

Leads

Hanshen Xiao

Research Areas

Impact Areas

Hanshen Xiao

Project

Programming Abstractions for Dynamic Graph Analytics

We plan to develop a programming abstraction to enable programmers to write efficient parallel programs to process dynamic graphs.

Julian Shun

Leads

Julian Shun

Research Areas

Impact Areas

Julian Shun

Project

Robot Compiler

The Robot Compiler allows non-engineering users to rapidly fabricate customized robots, facilitating the proliferation of robots in everyday life. It thereby marks an important step towards the realization of personal robots that have captured imaginations for decades.

Leads

Joseph DelPreto

Andrew Spielberg

Daniela Rus

Research Areas

Impact Areas

Internet of Things

Project

Starling: Query Optimization for Cloud Services

Data Systems Group

Starling is a scalable query execution engine built on cloud function services that computes at a fine granularity, helping people more easily match workload demand.

Sam Madden

Matthew Perron

Leads

Sam Madden

Matthew Perron

Research Areas

Computer Architecture

Impact Areas

Model-Based Embedded and Robotics Systems Group

Sam Madden

Matthew Perron

Project

Uhura: Personal Assistant that Manages Risk

Computation Structures Group

Uhura is an autonomous system that collaborates with humans in planning and executing complex tasks, especially under over-subscribed and risky situations.

Leads

Yuening Zhang

Brian Williams

Howard Shrobe

Research Areas

Impact Areas

Entertainment

Project

Video Data Querying

Data Systems Group

We are developing machine-learning technology to help users efficiently run data queries over large archives of raw video.

Sam Madden

Favyen Bastani

Leads

Sam Madden

Favyen Bastani

Research Areas

Impact Areas

Natural Language Processing Group

Entertainment

Transportation

Sam Madden

Favyen Bastani

24 People Results

PI
Core/Dual

Regina Barzilay

Professor

Natural language processing, machine learning

E regina@csail.mit.edu

T 258-5706

Lead

Aspect-Augmented Adversarial Networks for Domain Adaptation

Member

Applied Machine Learning Community of Research

Natural language processing, machine learning

Cenk Baykal

Graduate Student

E baykal@mit.edu

RM 32-376C

Member

Coresets for Machine Learning Algorithms

PI
Core/Dual

Costis Daskalakis

Associate Professor

Algorithms, Economics, Game Theory, Statistics, Machine Learning

E costis@csail.mit.edu

T 253-9643

Lead

Is the Casino using a Riffle Shuffle?

Understanding Convergence of Iterative Algorithms

Center for Deployable Machine Learning (CDML)

Member

Algorithms, Economics, Game Theory, Statistics, Machine Learning

Barış Ekim

Graduate Student

E ekim@csail.mit.edu

PI
Core/Dual

John Guttag

Professor

Machine learning, medicine, data-driven inference

E guttag@csail.mit.edu

T 253-6022

Lead

Data-Driven Inference Group

Hidden Influencers, Risk and Causes of Infection

Predicting Adverse Events Across Changing Electronic Health Record Systems

Machine Learning for Insights in Basketball Strategy

Detecting Voice Misuse to Diagnose Disorders

Member

Systems Community of Research

Machine learning, medicine, data-driven inference

PI
Core/Dual

Piotr Indyk

Professor

Algorithms, theory, computational geometry

E indyk@csail.mit.edu

T 452-3402

Lead

Sublinear/Streaming Algorithms for Covering Problem

Sublinear Algorithms for Massive Data Problems

Member

Algorithms, theory, computational geometry

PI
Core/Dual

Tommi Jaakkola

Professor

Machine learning, natural language processing, genomics

E tommi@csail.mit.edu

T 253-0440

Lead

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

Machine learning, natural language processing, genomics

Siddhartha Jayanti

Graduate Student

E siddhartha@csail.mit.edu

PI
Core/Dual

David Karger

Professor

Helping people manage information

E karger@csail.mit.edu

T 258-6167

Lead

Haystack 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

Systems Community of Research

Helping people manage information

PI
Core/Dual

Dina Katabi

Professor

Wireless, networks, machine learning

E dina@csail.mit.edu

T 324-6027

Lead

Networks at MIT

EQ-Radio: Emotion Recognition using Wireless Signals

Member

Computational Genomics Group

Wireless, networks, machine learning

Konstantin Krismer

Graduate Student

E krismer@mit.edu

Member

Lucas Liebenwein

Graduate Student

Student and Researcher | Robotics, AI | Advanced Autonomy

E lucasl@mit.edu

RM 32-376C

Lead

Safety Standards for Autonomous Vehicles

Member

Self-Driving Vehicles

Coresets for Machine Learning Algorithms

Student and Researcher | Robotics, AI | Advanced Autonomy

Slobodan Mitrovic

Postdoctoral Associate

E slobo@mit.edu

PI
Core/Dual

Ankur Moitra

Associate Professor

Theory, algorithms, machine learning, complexity

E moitra@csail.mit.edu

T 253-5876

Lead

Efficient Robust Estimation in High Dimensions

Member

Center for Deployable Machine Learning (CDML)

Cryptography and Information Security Group

Theory, algorithms, machine learning, complexity

PI
Core/Dual

Ronald Rivest

Institute Professor

Cryptography, cybersecurity, RSA public-key encryption

E rivest@mit.edu

T 253-5880

Lead

Member

Computing & Society Community of Research

Cryptography, cybersecurity, RSA public-key encryption

PI
Core/Dual

Ronitt Rubinfeld

Professor

Theory, algorithms

E ronitt@csail.mit.edu

T 253-0884

Lead

Sublinear/Streaming Algorithms for Covering Problem

Complexity Theory Group

Member

Safety Standards for Autonomous Vehicles

Theory, algorithms

Wilko Schwarting

Graduate Student

E wilkos@mit.edu

Lead

Planning in Congestion for Autonomous Vehicles

Socially-Compliant Behavior for Autonomous Driving

Member

Self-Driving Vehicles

Programming Abstractions for Dynamic Graph Analytics

PI
Core/Dual

Julian Shun

Assistant Professor

Parallel computing, applied algorithms, graph processing.

E jshun@mit.edu

T 258-0669

Lead

Bridging Theory and Practice in Shared-Memory Parallel Algorithm Design

High-Performance Parallel Clustering

Member

Data Systems Group

Systems Community of Research

Parallel computing, applied algorithms, graph processing.

PI
Core/Dual

Justin Solomon

Assistant Professor

Geometry, graphics, vision, machine learning, optimization.

E jsolomon@mit.edu

Lead

Computer Graphics 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.

PI
Core/Dual

Russ Tedrake

Professor

Robotics, machine learning, motion planning and control

E russt@csail.mit.edu

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

Neil Thompson

Research Scientist

I'm an innovation scholar studying computer science and economics.

E neil_t@mit.edu

Lead

Supertech Research Group

Determining Wikipedia's Influence on Science

I'm an innovation scholar studying computer science and economics.

Yu Wang

Graduate Student

E wangyu9@mit.edu

RM 32-D475A

Member