# Research

- Research Areas
- Impact Areas

1 Group Results matching all criteria

17 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

#### Research Group

## Haystack Group

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

#### Community of Research

## Human-Computer Interaction Community of Research

The focus of the HCI CoR is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

#### 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

## 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.

27 Project Results

#### Project

## 3D Generative Adversarial Networks

We study the problem of 3D object generation. We propose a novel framework, 3D Generative Adversarial Network (3D-GAN), leveraging recent advances in volumetric convolutional networks and generative adversarial nets.

#### 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

## Algorithmic Aspects of Performance Engineering

The project concerns algorithmic solutions for writing fast codes.

#### 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

## 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

## Computational Knitting: Patterning and Shaping for Everyone

Knitting is the new 3d printing. It has become popular again with the widespread availability of patterns and templates, together with the maker movements. Lower-cost industrial knitting machines are starting to emerge, but we are still missing the corresponding design tools. Our goal is to fill this gap.

#### 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

## 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

## 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

## Foundry: Design for Multi-Material 3-D Printing

A new approach for designs in multi-material fabrication

#### 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

## Helping robots learn using demonstrations

Help robots learn faster by providing demonstrations when they need help

#### Project

## High-Performance Parallel Clustering

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

#### Project

## InstantCAD: Interactive Design Exploration/Optimization for CAD Models

Almost every object we use is developed with computer-aided design (CAD). While CAD programs are good for creating designs, using them to actually improve existing designs can be difficult and time-consuming.

#### 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

#### Project

## Single-Image 3D Interpreter Network

We aim to understand 3D object structure from a single image. We propose an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, by training it on both real 2D-annotated images and synthetic 3D data and by integrating a 3D-to-2D projection layer.

36 People Results

## Cenk Baykal

Graduate Student

## Yu-An Chung

Graduate Student

## Adrian Dalca

Research Scientist

## Mustafa Doga Dogan

Graduate Student

## Tao Du

Graduate Student

## David Harwath

Research Scientist

## Evan Hernandez

Graduate Student

## Siddhartha Jayanti

Graduate Student

## Alexandre Kaspar

Graduate Student

## Kenji Kawaguchi

Graduate Student

## Lucas Liebenwein

Graduate Student

## Slobodan Mitrovic

Postdoctoral Associate

20 News Results

## Giving soft robots senses

In a pair of papers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), two teams enable better sense and perception for soft robotic grippers.

## Sprayable user interfaces

SprayableTech system lets users create large-scale interactive surfaces with sensors and displays using airbrushed inks

## Deep learning with point clouds

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

## 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.

## CSAIL hosts first-ever TEDxMIT

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

## Teaching artificial intelligence to connect senses like vision and touch

MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.

## MIT CSAIL holds trustworthy AI event with Microsoft

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

## 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.

## Teaching machines to see in 3-D

CSAIL’s approach uses algorithms and “2.5-D” sketches to let computers visualize images from any perspective

## Robots can now pick up any object after inspecting it

Breakthrough CSAIL system suggests robots could one day be able to see well enough to be useful in people’s homes and offices.

## Design tool reveals a product’s many possible performance tradeoffs

Users can quickly visualize designs that optimize multiple parameters at once.

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

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

## 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.

## 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.

## Automating materials design

With new approach, researchers specify desired properties of a material, and a computer system generates a structure accordingly.

## 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.

## Four from MIT named 2017 Association for Computer Machinery Fellows

Today four MIT faculty were named among the Association for Computer Machinery's 2017 Fellows for making “landmark contributions to computing.”

## 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.”

## 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.