# Research

- Research Areas
- Impact Areas

1 Group Results matching all criteria

18 Group Results

#### Research Group

## Algorithms Group

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

#### 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

## 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

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.

#### 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

## Quantum Information Science Group

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

#### Research Group

## Sensing, Learning and Inference

We focus on the analysis of complex, high-dimensional data.

#### 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

#### 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

27 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

## Algorithmic Aspects of Performance Engineering

The project concerns algorithmic solutions for writing fast codes.

#### 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

## 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

## Crowdsourcing in Graphics and Vision

Our goal is to develop new applications and algorithms that leverage the skills of distributed crowdworkers, notably for image and video processing applications.

#### 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

## 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

## Geometry and topology for scientific computing and shape analysis

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

#### Project

## Geometry in Large-Scale Machine Learning

Data often has geometric structure which can enable better inference; this project aims to scale up geometry-aware techniques for use in machine learning settings with lots of data, so that this structure may be utilized in practice.

#### Project

## High-Performance Parallel Clustering

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

#### Project

## Noria: a new data-flow system for web applications

We're developing a flexible, high-performance storage architecture for database-backed applications, based on a dynamic set of queries specified by the developer which Soup automatically optimizes.

#### Project

## Optimal transport for statistics and machine learning

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

#### 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.

#### 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.

#### Project

## Sensible Deep Learning for 3D Data

Developing state-of-the-art deep learning algorithms for analyzing and modeling 3D geometry

41 People Results

## Ibrahim Sabek

Postdoctoral Associate

## Wilko Schwarting

Graduate Student

## Malte Schwarzkopf

Research Affiliate

## Zeyuan Shang

Graduate Student

## Dmitriy Smirnov

Graduate Student

## Neil Thompson

Research Scientist

## Yu Wang

Graduate Student

## Paul Zhang

Graduate Student

8 News Results

## Data systems that learn to be better

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

## 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 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.

## 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.

## 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.

## 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.”

## Tax-evading corporations, watch out: our AI knows what you're doing

CSAIL researchers recently helped develop "STEALTH," a system that uses artificial intelligence to combat tax evasion by corporations.

## 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.