Research

  • Research Areas
  • Impact Areas

Algorithms & Theory

Foundational work that includes complexity, parallel computing and game theory

AI & Machine Learning

Spanning natural language processing, deep-learning, computer vision and more

Computational Biology

Understanding disease via epigenomics, gene regulation and bioinformatics

Computer Architecture

How CPUs, memory and other systems are designed and organized

Graphics & Vision

Teaching computers to interpret, create and animate visual data

Human-Computer Interaction

Software and hardware that let us naturally interact with technology

Programming Languages & Software Engineering

From compilers and verification to software design and engineering

Robotics

Vision, actuation, sensing and manipulation of machines

Security & Cryptography

Developing technologies to prevent and recover from cyber-attacks

Systems & Networking

From distributed systems and databases to wireless

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1 Group Results matching all criteria

2 Project Results matching all criteria

7 People Results matching all criteria

Kaelbling

PI
Core/Dual

Leslie Kaelbling

Professor
Robotics, machine learning, motion planning
Kaelbling
T 258-9695
RM 32-333

Lead

Learning and Intelligent Systems
Machine Learning
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, machine learning, motion planning
Lozano-Perez

PI
Core/Dual

Tomas Lozano-Perez

Professor
Robotics, artificial intelligence
Lozano-Perez
T 253-7889
RM 32-333

Lead

Learning and Intelligent Systems
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, artificial intelligence

4 News Results matching all criteria

6 Group Results

8 Project Results

Project

Computational Knitting: Patterning and Shaping for Everyone

Computational Fabrication Group
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.

Alexandre Kaspar

Leads

Alexandre Kaspar

Research Areas

Graphics & Vision
Human-Computer Interaction

Impact Areas

Entertainment
Manufacturing
 

Alexandre Kaspar

Project

PrivacyML - A Privacy Preserving Framework for Machine Learning

Decentralized Information Group
We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.
Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Leads

Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

Research Areas

AI & Machine Learning
Security & Cryptography

Impact Areas

Big Data
Cybersecurity
Education
Energy
Entertainment
Health Care
Internet of Things
Manufacturing
Transportation
 
Kagal-headshot

Lalana Kagal

Vaikkunth

Vaikkunth Mugunthan

18 People Results

Gregory Falco

Gregory Falco

Postdoctoral Associate
Dr. Falco is a hacker and expert in IoT and IIoT cybersecurity.
Gregory Falco
Dr. Falco is a hacker and expert in IoT and IIoT cybersecurity.
Kaelbling

PI
Core/Dual

Leslie Kaelbling

Professor
Robotics, machine learning, motion planning
Kaelbling
T 258-9695
RM 32-333

Lead

Learning and Intelligent Systems
Machine Learning
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, machine learning, motion planning
Lozano-Perez

PI
Core/Dual

Tomas Lozano-Perez

Professor
Robotics, artificial intelligence
Lozano-Perez
T 253-7889
RM 32-333

Lead

Learning and Intelligent Systems
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, artificial intelligence
Rus

PI
Core/Dual

Daniela Rus

Director
CSAIL director, robotics, AI, data science, privacy
Rus
T 258-7567
RM 32/ 32-368/ 374

Lead

Distributed Robotics Laboratory
Social Network Extraction from GPS Datasets with Coresets
Safety Standards for Autonomous Vehicles
Soft Contact Modeling
Practical Secure Computation
Self-Driving Vehicles
Robust Learning for Autonomous Vehicles
Brain-Controlled Robots
Planning in Congestion for Autonomous Vehicles
Socially-Compliant Behavior for Autonomous Driving
Coresets for Machine Learning Algorithms
M-Blocks Modular Robotics
Robot Compiler
Distributed Robot Garden
Route and Motion Planning for Mobility-on-Demand with Rules of the Road

Member

Social Network Extraction from GPS Datasets with Coresets
Task-Driven Co-Optimization Of Articulated Robots
Wearable Blind Navigation
Drones that Drive
Better Models for Ride-Sharing
SoFi - The Soft Robotic Fish
CSAIL director, robotics, AI, data science, privacy

18 News Results

Researchers at MIT’s Microsystems Technology Laboratories have built a low-power chip specialized for automatic speech recognition. With power savings of 90 to 99 percent, it could make voice control practical for relatively simple electronic devices.
AI & Machine Learning Human-Computer Interaction Energy Internet of Things Manufacturing

Voice control everywhere

The butt of jokes as little as 10 years ago, automatic speech recognition is now on the verge of becoming people’s chief means of interacting with their principal computing devices. In anticipation of the age of voice-controlled electronics, MIT researchers have built a low-power chip specialized for automatic speech recognition. Whereas a cellphone running speech-recognition software might require about 1 watt of power, the new chip requires between 0.2 and 10 milliwatts, depending on the number of words it has to recognize.

Voice control everywhere