Research

Impact Areas

Lead

Nancy Lynch

1 Project Results matching all criteria

Project

Algorithmic Aspects of Performance Engineering

Supertech Research Group

The project concerns algorithmic solutions for writing fast codes.

Leads

Research Areas

Impact Areas

1 People Results matching all criteria

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

Helping people manage information

13 Group Results

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.

Alan Edelman

Leads

Alan Edelman

Research Areas

Impact Areas

Lead

Alan Edelman

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 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

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

Computational Biology

Impact Areas

Health Care

Lead

Bonnie Berger

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

Computational Biology

Impact Areas

Health Care

Lead

Nir Shavit

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

Security & Cryptography

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

Graphics & Vision

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

Education

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

Leads

Charles E. Leiserson

Research Areas

Computer Architecture

Impact Areas

Lead

Charles E. Leiserson

Research Group

Theory of Computation Group

Theory research at CSAIL covers a broad spectrum of topics, including algorithms, complexity theory, cryptography, distributed systems, parallel computing and quantum computing.

Leads

Bonnie Berger

Costis Daskalakis

Erik Demaine

Alan Edelman

Michael Goemans

Shafi Goldwasser

Piotr Indyk

David Karger

Jonathan Kelner

Tom Leighton

Charles E. Leiserson

Nancy Lynch

Aleksander Madry

Albert R. Meyer

Silvio Micali

Ankur Moitra

Ronald Rivest

Ronitt Rubinfeld

Nir Shavit

Peter Shor

Vinod Vaikuntanathan

Ryan Williams

Virginia Vassilevska Williams

Research Areas

Impact Areas

Lead

Bonnie Berger

Costis Daskalakis

Erik Demaine

Alan Edelman

Michael Goemans

Shafi Goldwasser

Piotr Indyk

David Karger

Jonathan Kelner

Tom Leighton

Charles E. Leiserson

Nancy Lynch

Aleksander Madry

Albert R. Meyer

Silvio Micali

Ankur Moitra

Ronald Rivest

Ronitt Rubinfeld

Nir Shavit

Peter Shor

Vinod Vaikuntanathan

Ryan Williams

Virginia Vassilevska Williams

Research Group

Theory of Distributed Systems Group

Nancy Lynch

Leads

Nancy Lynch

Research Areas

Learning and Intelligent Systems

Impact Areas

Lead

Nancy Lynch

10 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.

Leads

Zi Wang

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Robotics

Impact Areas

Education

Entertainment

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.

Zi Wang

Leads

Zi Wang

Research Areas

Impact Areas

Education

Zi Wang

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

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

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

Security & Cryptography

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

Natural Language Processing Group

Julian Shun

17 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

Aspect-Augmented Adversarial Networks for Domain Adaptation

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

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

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

Aspect-Augmented Adversarial Networks for Domain Adaptation

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

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

Wireless, networks, machine learning

Kenji Kawaguchi

Graduate Student

E kawaguch@mit.edu

Lead

Learning and Optimization

Member

Learning and Intelligent Systems

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 Fellow

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

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

Cryptography and Information Security Group

Member

Keys Under Doormats

Cryptography, cybersecurity, RSA public-key encryption

PI
Core/Dual

Ronitt Rubinfeld

Professor

Theory, algorithms

E ronitt@csail.mit.edu

T 253-0884

Lead

Complexity Theory Group