# Research

- Research Areas
- Impact Areas

2 Group Results matching all criteria

#### 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.

2 Project Results matching all criteria

#### 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

## Geometry and topology for scientific computing and shape analysis

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

1 News Results matching all criteria

## Deep learning with point clouds

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

2 Group Results

#### 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

## Geometric Data Processing Group

4 Project Results

#### Project

## Geometry and topology for scientific computing and shape analysis

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

#### 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

## Sensible Deep Learning for 3D Data

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

24 People Results

## Cenk Baykal

Graduate Student

## Yu-An Chung

Graduate Student

## Adrian Dalca

Research Scientist

## Barış Ekim

Graduate Student

## Evan Hernandez

Graduate Student

## Siddhartha Jayanti

Graduate Student

## Lucas Liebenwein

Graduate Student

## Slobodan Mitrovic

Postdoctoral Associate

## Wilko Schwarting

Graduate Student

## Dmitriy Smirnov

Graduate Student

## Neil Thompson

Research Scientist

## Yu Wang

Graduate Student

## Paul Zhang

Graduate Student

6 News Results

## 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.”

## 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.