# Research

- Research Areas
- Impact Areas

2 Group Results matching all criteria

#### 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.

24 Group Results

#### Research Group

## Advanced Network Architecture Group

The challenge that motivates the ANA group is to foster a healthy future for the Internet. The interplay of private sector investment, public sector regulation and public interest advocacy, as well as the global diversity in drivers and aspirations, makes for an uncertain future.

#### Research Group

## Algorithms Group

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

#### 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.

#### 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.

#### 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.

#### 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.

#### Research Group

## Computation Structures Group

Our mission is fostering the creation and development of high-performance, reliable and secure computing systems that are easy to interact with.

#### 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.

#### Community of Research

## Computing & Society Community of Research

#### 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.

#### Research Group

## Data Systems Group

We conduct research on all areas of database systems and information management.

#### Research Group

## Decentralized Information Group

We are investigating decentralized technologies that affect social change.

#### Research Group

## Geometric Data Processing Group

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

#### Research Group

## Haystack Group

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

#### Research Group

## Networks at MIT

We build new protocols and architectures to improve the robustness and performance of computer networks. We develop practical solutions in wireless networks, network security, traffic engineering, congestion control, and routing.

#### Research Group

## Networks and Mobile Systems

We conduct research in many areas of networking: wireless networks, Internet architecture and protocols, overlay and peer-to-peer networks, sensor networks, network security, and networked systems.

#### Research Group

## Quantum Information Science Group

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

#### Research Group

## Supertech Research Group

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

#### 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.

#### 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.

#### 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.

#### 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.

#### Research Group

## World Wide Web Consortium

Led by Web inventor and Director, Tim Berners-Lee and CEO Jeff Jaffe, the W3C focus is on leading the World Wide Web to its full potential by developing standards, protocols and guidelines that ensure the long-term growth of the Web

33 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.

#### Project

## AdaptDB: Adaptive Partitioning for Distributed Joins

Our goal is to develop an adaptive storage manager for analytical database workloads in a distributed setting. It works by partitioning datasets across a cluster and incrementally refining data partitioning as queries are run.

#### Project

## Aggregate Congestion Control

Develop a method for sending persistently backlogged traffic on the Internet while adapting to link conditions dynamically.

#### Project

## Algorithmic Aspects of Performance Engineering

The project concerns algorithmic solutions for writing fast codes.

#### Project

## An Interlock for Self Driving Cars

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.

#### Project

## Aurum: Large Scale Data Discovery

Aurum is a data discovery system that works at large scale, helping people find relevant data.

#### Project

## Bayesian Optimization for Global Optimization of Expensive Black-box Functions

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.

#### 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.

#### Project

## BlueDBM: Distributed Flash Storage for Big Data Analytics

BlueDBM is an architecture of computer clusters consisting of fast distributed flash storage and in-storage accelerators, which often outperforms larger and more expensive clusters in applications such as graph analytics.

#### 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.

#### Project

## The Car Can Explain!

Developing techniques to allow self-driving cars and other AI-driven systems to explain behaviors and failures.

#### Project

## Compression and Reordering for Parallel Graph Analytics

We plan to develop a suite of graph compression and reordering techniques as part of the Ligra parallel graph processing framework to reduce space usage and improve performance of graph algorithms.

#### 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.

#### Project

## Data Civilizer

Data scientists universally report that they spend at least 80% of their time finding data sets of interest, accessing them, cleaning them and assembling them into a unified whole.

#### Project

## Data Warehouse Construction

Historically, DBMSs in the warehouse space partitioned their data across a shared nothing

cluster.

cluster.

#### Project

## Database Design

The conventional wisdom described in all text books for performing database design is never followed in practice.

#### Project

## Determining Wikipedia's Influence on Science

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.

#### Project

## Deterministic Algorithms for Robotic Task and Motion Planning

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

#### 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.

With strong relationship to generalized Chinese Remainder Theorem, the geometry properties in the remainder code space, a special lattice space, are explored.

#### 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.

## Suvrit Sra

#### Project

## Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

We are studying how best to implement bilateral control and make it acceptable to drivers

#### Project

## Geometry and topology for scientific computing and shape analysis

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

#### Project

## High-Performance Parallel Clustering

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

39 People Results

## Cenk Baykal

Graduate Student

## Jonathan Behrens

Graduate Student

## Siddhartha Jayanti

Graduate Student

## Fredrik Berg Kjolstad

Graduate Student

## David Lazar

Graduate Student

## Lucas Liebenwein

Graduate Student

## Slobodan Mitrovic

Postdoctoral Associate

## Seo Jin Park

Postdoctoral Assoicate

## Ibrahim Sabek

Postdoctoral Associate

31 News Results

## Data systems that learn to be better

Storage tool adapts to what its datasets’ users want to search.

## CSAIL device lets doctors monitor COVID-19 patients from a distance

Wireless system helps Boston retirement home care for COVID patients while reducing risk of contagion

## Deep learning with point clouds

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

## 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.

## Making it easier to program and protect the web

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

## 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.

## CSAIL hosts first-ever TEDxMIT

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

## MIT CSAIL holds trustworthy AI event with Microsoft

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

## Engineers program marine robots to take calculated risks

Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.

## 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.

## Identifying artificial intelligence “blind spots”

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

## Cambridge Mobile Telematics Raises $500M from SoftBank Vision Fund

Cambridge Mobile Telematics Raises $500M from SoftBank Vision Fund

## A step toward personalized, automated smart homes

System that automatically identifies people moving around indoors could enable self-adjusting homes.

## Model helps robots navigate more like humans do

In simulations, robots move through new environments by exploring, observing, and drawing from learned experiences.

## Demaine gives Congressional briefing on intersection of origami and computer science

MIT professor discusses using paper-folding for applications in manufacturing, medicine, and robotics

## Making driverless cars change lanes more like human drivers do

Algorithm computes “buffer zones” around autonomous vehicles and reassess them on the fly.

## Building AI systems that make fair decisions

Harini Suresh, a PhD student at MIT CSAIL, studies how to make machine learning algorithms more understandable and less biased.

## MIT professor wins Association for Computing Machinery Prize in Computing

CSAIL researcher is honored for her contributions to wireless systems

## Programming drones to fly in the face of uncertainty

CSAIL's NanoMap system enables drones to avoid obstacles while flying at 20 miles per hour, by more deeply integrating sensing and control.

## Tracking patients’ progress with radio signals and machine learning

Novartis researchers leverage in-house startup initiative to begin digital technology research collaboration.

## 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.

## Improving traffic - by tailgating less

New CSAIL work shows that traffic would flow faster if drivers kept an equal distance between cars

## 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.”

## 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.