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7 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.

+5

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Algorithms & Theory

Graphics & Vision

Human-Computer Interaction

Programming Languages & Software Engineering

Security & Cryptography

Impact Areas

Lead

Aleksander Madry

Aleksander Madry

Research Group

Geometric Data Processing Group

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

+6

Justin Solomon

Leads

Justin Solomon

Research Areas

Algorithms & Theory

Graphics & Vision

Impact Areas

Lead

Justin Solomon

Justin Solomon

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.

+4

Stefanie Mueller

Leads

Stefanie Mueller

Research Areas

Graphics & Vision

Human-Computer Interaction

Programming Languages & Software Engineering

Impact Areas

Lead

Stefanie Mueller

Stefanie Mueller

Research Group

Sensing, Learning and Inference

We develop scalable algorithms for analysis of complex, high-dimensional data.

+6

John Fisher

Leads

John Fisher

Research Areas

Graphics & Vision

Impact Areas

Internet of Things

Lead

John Fisher

John Fisher

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.

+7

Martin Rinard

Leads

Martin Rinard

Research Areas

Algorithms & Theory

Graphics & Vision

Programming Languages & Software Engineering

Security & Cryptography

Systems & Networking

Impact Areas

Internet of Things

Lead

Martin Rinard

Martin Rinard

Research Group

Vision Group

Our researchers create state-of-the-art systems to better recognize objects, people, scenes, behaviors and more, with applications in health-care, gaming, tagging systems and more.

Leads

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Antonio Torralba

Research Areas

Graphics & Vision

Impact Areas

Lead

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Antonio Torralba

Community of Research

Visual Computing Community of Research

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

+10

William Freeman

Leads

William Freeman

Research Areas

Graphics & Vision

Impact Areas

Lead

William Freeman

William Freeman

12 Project Results

Project

A Counterfactual Simulation Model of Causal Judgment

Computational Cognitive Science Group

Cognitive AI Community of Research

We develop a computational model that explains how people make causal judgments in physical scenes by mentally simulating counterfactual outcomes and comparing those to what actually happened.

Leads

Research Areas

Graphics & Vision

Impact Areas

Project

AIRvatar System: A Framework for Analyzing Virtual Identities

Imagination, Computation, and Expression Laboratory (ICE Lab)

Human-Computer Interaction Community of Research

AIRvatar is a system that telemetrically collects and analyzes fine-grained data on users’ virtual identities and the process used to create them.

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Graphics & Vision

Human-Computer Interaction

Impact Areas

D. Fox Harrell

Project

Artificial Co-driver

Computer Graphics Group

By observing driver in real road situations, we learn a computer assistant that can make driving easier, safer and more enjoyable.

Leads

Research Areas

Graphics & Vision

Impact Areas

Project

Characterizing the Sensitivity of Vision-in-the-Loop Driving Controllers Using CG

Computer Graphics Group

We work on understanding and improving vision algorithms used for self driving cars, by introducing synthetic data-sets to the loop.

Leads

Research Areas

Graphics & Vision

Impact Areas

Project

Computational Multicopter Design

Computer Graphics Group

A system that can help you to design our own drone!

Tao Du

Leads

Tao Du

Research Areas

Graphics & Vision

Impact Areas

Tao Du

Project

Crowdsourcing in Graphics and Vision

Computational Design and Fabrication Group

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

Alexandre Kaspar

Leads

Alexandre Kaspar

Research Areas

Graphics & Vision

Human-Computer Interaction

Impact Areas

Alexandre Kaspar

Project

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

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

Berthold Horn

Leads

Berthold Horn

Research Areas

Graphics & Vision

Systems & Networking

Impact Areas

Berthold Horn

Project

Geometry in Large-Scale Machine Learning

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.

Justin Solomon

Stefanie Jegelka

Leads

Justin Solomon

Stefanie Jegelka

Research Areas

Graphics & Vision

Impact Areas

Internet of Things

Justin Solomon

Stefanie Jegelka

Project

Peripheral Vision and Driver Behavior

Our goal is to understand what drivers can perceive when they are not attending to the road ahead and the consequences of their perceptions for safety.

Leads

Research Areas

Graphics & Vision

Impact Areas

Project

Reliable and Robust Machine Learning

Algorithms Group

We work towards a principled understanding of the current machine learning toolkit and making this toolkit be robust and reliable.

+4

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Algorithms & Theory

Graphics & Vision

Security & Cryptography

Impact Areas

Aleksander Madry

Project

Wearable Blind Navigation

Distributed Robotics Laboratory

We enable independent navigation for visually impaired users through a wearable feedback system using time-of-light sensors or depth cameras.

Leads

Research Areas

Graphics & Vision

Human-Computer Interaction

Impact Areas

Project

Where Are They Looking?

Our goal is to build a system that predicts where people are looking in images. Given an image and the location of a head, our approach follows the gaze of the person and identifies the object being looked at.

Leads

Research Areas

Graphics & Vision

Impact Areas

10 People Results

PI
Core/Dual

Ted Adelson

Professor

Vision, touch sensing, robotics

E adelson@csail.mit.edu

T 253-0645

RM

Lead

Blood Pressure Imager

Member

Embodied Intelligence Community of Research

Visual Computing Community of Research

Vision, touch sensing, robotics

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

RM

Member

HCI Engineering Group

PI
Core/Dual

Fredo Durand

Professor

Vision, graphics, computational photography

E fredo@csail.mit.edu

T 253-7223

RM

Lead

Computer Graphics Group

Human-Computer Interaction

Computational Understanding of Visualizations and Graphic Designs

Member

Computational Bounce Flash for Indoor Portraits

Visual Computing Community of Research

Vision, graphics, computational photography

PI
Core/Dual

William Freeman

Professor

Vision, machine learning, Bayesian modeling

E billf@csail.mit.edu

T 253-8828

RM

Lead

Computer Graphics Group

Visual Computing Community of Research

Member

Single-Image 3D Interpreter Network

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

Learning the Arrow of Time from Videos

3D Generative Adversarial Networks

Embodied Intelligence Community of Research

Vision, machine learning, Bayesian modeling

PI
Core/Dual

Berthold Horn

Professor

Vision, computational imaging

E bkph@csail.mit.edu

T 253-5863

RM

Lead

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Member

Visual Computing Community of Research

Vision, computational imaging

Fern Keniston

Senior Administrative Assistant

Research and Education Support

E fern@csail.mit.edu

T 253-5860

RM 32/ 32-250A/ 391

Member

Clinical Decision-Making Group

Research and Education Support

PI
Core/Dual

John Leonard

Professor

Robotics, navigation/mapping, self-driving cars

E jleonard@csail.mit.edu

T 253-5305

RM

Lead

Marine Robotics Group

Self-Driving Vehicles

Member

Embodied Intelligence Community of Research

Robotics, navigation/mapping, self-driving cars

PI
Core/Dual

Antonio Torralba

Professor

Vision, graphics, perception, machine learning

E torralba@csail.mit.edu

T 324-0900

RM 32-386G

Lead

Understanding Light via Deep Neural Networks

Understandable Deep Networks

Understanding Food images

Member

Predictive Vision

Where Are They Looking?

Single-Image 3D Interpreter Network

VirtualHome: Representing Activities as Programs

Places Database for Scene Recognition

Semantic Scene Understanding through ADE20K dataset

Crossing the Vision-Language Boundary

Center for Deployable Machine Learning (CDML)

Visual Computing Community of Research

Embodied Intelligence Community of Research

Vision, graphics, perception, machine learning

Dishita Turakhia

Graduate Student

PhD student in HCIE group at CSAIL

E dishita@mit.edu

RM

Member

HCI Engineering Group

PhD student in HCIE group at CSAIL

Yu Wang

Graduate Student

E wangyu9@mit.edu

RM 32-D475A

Member

Geometric Data Processing Group

Geometry and topology for scientific computing and shape analysis

8 News Results

point cloud 1

Algorithms & Theory AI & ML Graphics & Vision Big Data Transportation

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

Barbara Liskov 2

Algorithms & Theory AI & ML 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

3d printed mosculp

Graphics & Vision Education Entertainment

September 19 '18

Creating 3-D-printed “motion sculptures” from 2-D videos

CSAIL system could help athletes, dancers, and others better analyze how they move

Creating 3-D-printed “motion sculptures” from 2-D videos

Given an image of someone doing yoga, the system can generate an image of them doing golf (and vice versa)

AI & ML Graphics & Vision Education

June 18 '18

Put any person in any pose

MIT CSAIL neural network could be used by coaches to help players see themselves using the correct form

Put any person in any pose

self driving

AI & ML Graphics & Vision Robotics Transportation

May 07 '18

Self-driving cars for country roads

For all the progress made in self-driving technologies, there still aren’t many places where they can actually drive. Companies like Google only test their fleets in major cities where they’ve spent countless hours meticulously labeling the exact 3-D positions of lanes, curbs, off-ramps, and stop signs.

Self-driving cars for country roads

Professor Berthold Horn and postdoctoral associate Liang Wang

Algorithms & Theory Graphics & Vision Transportation

December 14 '17

Improving traffic - by tailgating less

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

Improving traffic - by tailgating less

A new system from MIT's Computer Science and Artificial Intelligence Laboratory works by analyzing light at the edge of walls, which is impacted by the reflections of objects around the corner from the camera.

AI & ML Graphics & Vision Transportation

October 09 '17

An algorithm for your blind spot

Light lets us see the things that surround us, but what if we could also use it to see things hidden around corners? It sounds like science fiction, but that’s the idea behind a new algorithm out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) — and its discovery has implications for everything from emergency response to self-driving cars.

An algorithm for your blind spot

System directs camera-equipped drones to maintain framing of an aerial shot.

Algorithms & Theory AI & ML Graphics & Vision Robotics Entertainment Transportation

May 17 '17

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.

Cinematography on the fly