Research

Impact Areas

Lead

Lalana Kagal

Tim Berners-Lee

Lalana Kagal

Tim Berners-Lee

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

Multimodal Understanding Group

Our objective is to build techniques, software, and hardware that enable natural interaction with
computation.

Randall Davis

Leads

Randall Davis

Research Areas

Impact Areas

Lead

Randall Davis

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

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

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

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

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

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

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

Lead

Peter Shor

Research Group

Multicore Algorithmics

We develop techniques for designing, implementing, and reasoning about multiprocessor algorithms, in particular concurrent data structures for multicore machines and the mathematical foundations of the computation models that govern their behavior.

Nir Shavit

Leads

Nir Shavit

Research Areas

Impact Areas

Lead

Nir Shavit

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

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

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

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

Lecturers

Lecturers in CSAIL

Leads

Research Areas

Impact Areas

Natural Language Processing Group

56 Project Results matching all criteria

Project

Aspect-Augmented Adversarial Networks for Domain Adaptation

We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.

Regina Barzilay

Tommi Jaakkola

Leads

Regina Barzilay

Tommi Jaakkola

Research Areas

Regina Barzilay

Tommi Jaakkola

Project

Matrix Permanents and Linear Optics

Complexity Theory Group

We use tools from quantum physics to prove new results in classical complexity.

Leads

Research Areas

Project

Deep Inverse Planning

Deep inverse planning for learning from high-dimensional demonstrations

Leads

Research Areas

Project

Multi-Core Data Structures

Multicore Algorithmics

We aim to develop data structures optimized for large-scale multi-core computers.

Nir Shavit

Leads

Nir Shavit

Research Areas

Nir Shavit

Project

Distributed Computation in Ant Colonies

We are interested in applying insights from distributed computing theory to understand how ants and other social insects work together to perform complex tasks such as foraging for food, allocating tasks to workers, and choosing high quality nest sites.

Nancy Lynch

Leads

Nancy Lynch

Research Areas

Nancy Lynch

Project

Social Network Extraction from GPS Datasets with Coresets

We extract the underlying hidden relations from the given location-based datasets (e.g. GPS data) and we estimate (approximate) the hidden a social network in the data by using a particular data reduction algorithm (i.e., by using coresets).

Daniela Rus

Leads

Daniela Rus

Research Areas

Model-Based Embedded and Robotics Systems Group

Daniela Rus

Project

Uhura: Personal Assistant that Manages Risk

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

Leads

Nikhil Bhargava

Yuening Zhang

Brian Williams

Research Areas

Impact Areas

Computational Connectomics Group

Manufacturing

Project

Reconstructing Neural Circuits from Mammalian Brain

We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.

Yaron Meirovitch

Nir Shavit

Leads

Yaron Meirovitch

Nir Shavit

Research Areas

Impact Areas

Yaron Meirovitch

Nir Shavit

Project

Performance Engineering of Cache Profilers

Supertech Research Group

Our goal is to develop lightweight tools that allow programmers to better understand the cache performance of their applications. Tasks include designing profilers, performance engineering existing ones, and exploring different metrics for cache interactions.

Helen Xu

Charles E. Leiserson

Leads

Helen Xu

Charles E. Leiserson

Research Areas

Helen Xu

Charles E. Leiserson

Project

Generating Good Adversarial Examples for Neural Networks

Robot Locomotion Group

Our goal is to better understand adversarial examples by 1) bounding the minimum perturbation that needs to be added to a regular input example to cause a given neural network to misclassify it, and 2) generating some adversarial input example with minimum perturbation.

Leads

Research Areas

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

Manufacturing

Data-Driven Inference Group

Project

Hidden Influencers, Risk and Causes of Infection

We aim to study the causes and transmission modes of infectious diseases among members of a community in the presence of hidden, asymptomatic spreaders of the pathogen.

Maggie Makar

John Guttag

Leads

Maggie Makar

John Guttag

Research Areas

Impact Areas

Data-Driven Inference Group

Maggie Makar

John Guttag

Project

Predicting Adverse Events Across Changing Electronic Health Record Systems

Transitioning machine learning models across electronic health record (EHR) versions can be improved by mapping different EHR encodings to a common vocabulary.

John Guttag

Peter Szolovits

Leads

John Guttag

Peter Szolovits

Research Areas

Impact Areas

John Guttag

Peter Szolovits

Project

Safety Standards for Autonomous Vehicles

In this project, we aim to develop a framework that can ensure and certify the safety of an autonomous vehicle. By leveraging research from the area of formal verification, this framework aims to assess the safety, i.e., free of collisions, of a broad class of autonomous car controllers/planners for a given traffic model.

Leads

Lucas Liebenwein

Wilko Schwarting

Daniela Rus

Research Areas

Impact Areas

Project

Algorithmic Aspects of Performance Engineering

Supertech Research Group

The project concerns algorithmic solutions for writing fast codes.

Leads

Research Areas

Impact Areas

Cryptography and Information Security Group

Project

Data Garbling: Computing on Encrypted Data

We are investigating the limits of computing on encrypted data, with a focus on the private outsourcing of computation over sensitive data.

Leads

Research Areas

Impact Areas

Project

Efficient Robust Estimation in High Dimensions

We are developing robust estimators for multivariate distributions which are both computationally efficient and near-optimal in terms of their accuracy. Our focus is on methods which are both theoretically sound and practically effective.

Jerry Li

Ankur Moitra

Leads

Jerry Li

Ankur Moitra

Research Areas

Jerry Li

Ankur Moitra

Project

OpenTuner: An Extensible Framework for Program Autotuning

Commit Group

OpenTuner is a new framework for building domain-specific multi-objective program autotuners.

Leads

Research Areas

Impact Areas

Project

Drones that Drive

Multi-robot path planning for robot swarms that can both fly and drive

Brandon Araki

Leads

Brandon Araki

Research Areas

Impact Areas

Brandon Araki

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

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

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

Planning under uncertainty with complex dynamics

We focus on learning to compute near-optimal plans which leverage environmental contact to mitigate action uncertainty, in hopes of enabling inexpensive robotic manipulators to perform precise assembly tasks.

Leads

Research Areas

Project

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Self-driving cars themselves can solve traffic problems even without global control.

Leads

Research Areas

2 People Results matching all criteria

Neil Thompson

Research Scientist

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

E neil_t@mit.edu

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

Longfei Zhou

Postdoctoral Fellow

Postdoc Fellow, computer vision, MIT.

E longfei@csail.mit.edu

Postdoc Fellow, computer vision, MIT.

55 News Results matching all criteria

Algorithms & Theory AI & Machine Learning Robotics

November 13 '19

Autonomous system improves environmental sampling at sea

Robotic boats could more rapidly locate the most valuable sampling spots in uncharted waters.

Autonomous system improves environmental sampling at sea

Algorithms & Theory AI & Machine Learning

October 29 '19

Pushy robots learn the fundamentals of object manipulation

Systems “learn” from novel dataset that captures how pushed objects move, to improve their physical interactions with new objects.

Pushy robots learn the fundamentals of object manipulation

Algorithms & Theory AI & Machine Learning Robotics

October 29 '19

Deep learning with point clouds

Research aims to make it easier for self-driving cars, robotics, and other applications to understand the 3D world.

Deep learning with point clouds

Algorithms & Theory Health Care

October 29 '19

Recovering “lost dimensions” of images and video

Model could recreate video from motion-blurred images and “corner cameras,” may someday retrieve 3D data from 2D medical images.

Recovering “lost dimensions” of images and video

Algorithms & Theory AI & Machine Learning Robotics Transportation

August 29 '19

MIT’s fleet of autonomous boats can now shapeshift

New capabilities allow “roboats” to change configurations to form pop-up bridges, stages, and other structures.

MIT’s fleet of autonomous boats can now shapeshift

Algorithms & Theory AI & Machine Learning Energy

August 28 '19

Artificial intelligence could help data centers run far more efficiently

MIT system “learns” how to optimally allocate workloads across thousands of servers to cut costs, save energy.

Artificial intelligence could help data centers run far more efficiently

Algorithms & Theory AI & Machine Learning Graphics & Vision

August 22 '19

Why did my classifier just mistake a turtle for a rifle?

Two longtime friends explore how computer vision systems go awry.

Why did my classifier just mistake a turtle for a rifle?

Algorithms & Theory AI & Machine Learning Graphics & Vision Programming Languages & Software Engineering Systems & Networking Entertainment Manufacturing

August 05 '19

Computer-aided knitting

New CSAIL research uses machine learning to customize clothing designs

Computer-aided knitting

Algorithms & Theory Security & Cryptography Transportation

July 23 '19

Making it easier to program and protect the web

Professor Adam Chlipala builds tools to help programmers more quickly generate optimized, secure code.

Making it easier to program and protect the web

Algorithms & Theory Graphics & Vision Robotics Manufacturing

July 22 '19

Automated system generates robotic parts for novel tasks

When designing actuators involves too many variables for humans to test by hand, this system can step in.

Automated system generates robotic parts for novel tasks

Algorithms & Theory AI & Machine Learning Computational Biology Computer Architecture Graphics & Vision Human-Computer Interaction Programming Languages & Software Engineering Robotics Security & Cryptography Systems & Networking Cybersecurity Education Energy Entertainment Health Care Manufacturing Transportation Wireless

June 27 '19

CSAIL hosts first-ever TEDxMIT

Speakers — all women — discuss everything from gravitational waves to robot nurses

CSAIL hosts first-ever TEDxMIT

Algorithms & Theory AI & Machine Learning Health Care

June 27 '19

Drag-and-drop data analytics

System lets nonspecialists use machine-learning models to make predictions for medical research, sales, and more.

Drag-and-drop data analytics

Algorithms & Theory Security & Cryptography

June 27 '19

Automated cryptocode generator is helping secure the web

System automatically writes optimized algorithms to encrypt data in Google Chrome browsers and web applications.

Automated cryptocode generator is helping secure the web

Algorithms & Theory Robotics Transportation

June 27 '19

Autonomous boats can target and latch onto each other

Fleet of “roboats” could collect garbage or self-assemble into floating structures in Amsterdam’s many canals.

Autonomous boats can target and latch onto each other

Algorithms & Theory AI & Machine Learning Health Care

June 25 '19

From one brain scan, more information for medical artificial intelligence

System helps machine-learning models glean training information for diagnosing and treating brain conditions Home Node From one brain scan, more information for medical artificial intelligence

From one brain scan, more information for medical artificial intelligence

Algorithms & Theory AI & Machine Learning

June 04 '19

Q&A: Phillip Isola on the art and science of generative models

Image-translation pioneer discusses the past, present, and future of generative adversarial networks, or GANs.

Q&A: Phillip Isola on the art and science of generative models

Algorithms & Theory Computational Biology

May 08 '19

CSAIL's Daskalakis wins ACM Grace Murray Hopper Award

Constantinos (“Costis”) Daskalakis, an MIT professor and CSAIL principal investigator, has won the 2018 ACM Grace Murray Hopper Award.

CSAIL's Daskalakis wins ACM Grace Murray Hopper Award

Algorithms & Theory

April 18 '19

A novel data-compression technique for faster computer programs

Researchers free up more bandwidth by compressing “objects” within the memory hierarchy.

A novel data-compression technique for faster computer programs

Algorithms & Theory Energy

April 02 '19

Advance boosts efficiency of flash storage in data centers

New architecture promises to cut in half the energy and physical space required to store and manage user data.

Advance boosts efficiency of flash storage in data centers

Algorithms & Theory Computational Biology Health Care

March 26 '19

Model learns how individual amino acids determine protein function

Technique could improve machine-learning tasks in protein design, drug testing, and other applications.

Model learns how individual amino acids determine protein function

Algorithms & Theory AI & Machine Learning

March 22 '19

“Particle robot” works as a cluster of simple units

Loosely connected disc-shaped “particles” can push and pull one another, moving en masse to transport objects.

“Particle robot” works as a cluster of simple units

Algorithms & Theory Systems & Networking

February 28 '19

Achieving greater efficiency for fast data center operations

System better allocates time-sensitive data processing across cores to maintain quick user-response times.

Achieving greater efficiency for fast data center operations

Algorithms & Theory AI & Machine Learning Cybersecurity Health Care Internet of Things Manufacturing Transportation Wireless

February 12 '19

MIT CSAIL holds trustworthy AI event with Microsoft

Workshop explores technical directions for making AI safe, fair, and understandable

MIT CSAIL holds trustworthy AI event with Microsoft

Algorithms & Theory AI & Machine Learning

February 05 '19

Putting neural networks under the microscope

Researchers pinpoint the “neurons” in machine-learning systems that capture specific linguistic features during language-processing tasks.

Putting neural networks under the microscope

16 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

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 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

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

Lecturers

Lecturers in CSAIL

Leads

Research Areas

Impact Areas

Research Group

Multicore Algorithmics

Nir Shavit

Leads

Nir Shavit

Research Areas

Impact Areas

Lead

Nir Shavit

Research Group

Multimodal Understanding Group

Our objective is to build techniques, software, and hardware that enable natural interaction with
computation.

Randall Davis

Leads

Randall Davis

Research Areas

Impact Areas

Lead

Randall Davis

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

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

Computation Structures Group

Impact Areas

Lead

Nancy Lynch

56 Project Results

Project

A new way of handling all-to-all broadcast

We design a new all-to-all broadcasts scheme with significantly less communication cost using aggregate signatures.

Alin Tomescu

Jun Wan

Leads

Jun Wan

Research Areas

Alin Tomescu

Jun Wan

Project

A Simplified and Extensible Cilk Runtime for Research

CilkS is a new runtime system for the Cilk multithreaded programming environment which makes it easy to experiment with new algorithms, data structures, and programming linguistics.

Charles E. Leiserson

Tao Schardl

Leads

Charles E. Leiserson

Tao Schardl

Research Areas

Charles E. Leiserson

Tao Schardl

Project

Active Learning of Models for Planning

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.

Leads

Zi Wang

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Project

Algebraic Techniques for Algorithm Design

We work on improving the algorithms for algebraic problems like matrix multiplication, and using these to design algorithms for fundamental non-algebraic problems.

Virginia Vassilevska Williams

Leads

Virginia Vassilevska Williams

Research Areas

Virginia Vassilevska Williams

Project

Algorithmic Aspects of Performance Engineering

Supertech Research Group

The project concerns algorithmic solutions for writing fast codes.

Leads

Research Areas

Impact Areas

Project

An Algorithmic Theory of Brain Networks

We are developing an algorithmic theory for brain networks, based on simple synchronized stochastic graph-based neural network models.

Nancy Lynch

Leads

Nancy Lynch

Research Areas

Nancy Lynch

Project

Approximating the diameter of a directed graph

There is a family of approximation algorithms for computing the diameter of an undirected graph that give a time/accuracy trade-off and our goal is to extend these results to directed graphs.

Virginia Vassilevska Williams

Leads

Virginia Vassilevska Williams

Research Areas

Natural Language Processing Group

Virginia Vassilevska Williams

Project

Aspect-Augmented Adversarial Networks for Domain Adaptation

Regina Barzilay

Tommi Jaakkola

Leads

Regina Barzilay

Tommi Jaakkola

Research Areas

Cryptography and Information Security Group

Regina Barzilay

Tommi Jaakkola

Project

Basing Cryptography on Structured Hardness

We aim to base a variety of cryptographic primitives on complexity theoretic assumptions. We focus on the assumption that there exist highly structured problems --- admitting so called "zero-knowledge" protocols --- that are nevertheless hard to compute

Leads

Research Areas

Project

Bayesian Optimization for Global Optimization of Expensive Black-box Functions

Machine Learning

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

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

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

Cryptography and Information Security Group

Daniela Rus

Project

Data Garbling: Computing on Encrypted Data

We are investigating the limits of computing on encrypted data, with a focus on the private outsourcing of computation over sensitive data.

Leads

Research Areas

Impact Areas

Project

Deep Inverse Planning

Deep inverse planning for learning from high-dimensional demonstrations

Leads

Research Areas

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

Manufacturing

Project

Distributed Algorithms for Dynamic and Noisy Platforms

Distributed systems are now everywhere, for example, in wireless communication networks, distributed data-management systems, coordinated robots, transportation systems, and modern multiprocessors.

Nancy Lynch

Leads

Nancy Lynch

Research Areas

Nancy Lynch

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

Distributed Computation in Ant Colonies

Nancy Lynch

Leads

Nancy Lynch

Research Areas

Nancy Lynch

Project

Distributed Robot Garden

The robot garden provides an aesthetically pleasing educational platform that can visualize computer science concepts and encourage young students to pursue programming and robotics.

Leads

Joseph DelPreto

Lindsay Sanneman

Daniela Rus

Research Areas

Impact Areas

Project

Diversity-inducing Probability Measures

Machine Learning

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

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Self-driving cars themselves can solve traffic problems even without global control.

Leads

Research Areas

Project

Drones that Drive

Multi-robot path planning for robot swarms that can both fly and drive

Brandon Araki

Leads

Brandon Araki

Research Areas

Impact Areas

Brandon Araki

Project

Efficient Robust Estimation in High Dimensions

Jerry Li

Ankur Moitra

Leads

Jerry Li

Ankur Moitra

Research Areas