Research

Impact Areas

Lead

Daniela Rus

+13

Daniela Rus

Research Group

Learning and Intelligent Systems

We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Lead

Leslie Kaelbling

Tomas Lozano-Perez

Leslie Kaelbling

Tomas Lozano-Perez

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.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Lead

Aleksander Madry

Research Group

Sensing, Learning and Inference

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

John Fisher

Leads

John Fisher

Research Areas

Impact Areas

Internet of Things

Lead

John Fisher

8 Project Results matching all criteria

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.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Leslie Kaelbling

Tomas Lozano-Perez

Project

SoFi - The Soft Robotic Fish

Exploration of underwater life with an acoustically controlled soft robotic fish

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Impact Areas

Joseph DelPreto

Project

Drones that Drive

Multi-robot path planning for robot swarms that can both fly and drive

Leads

Research Areas

Impact Areas

Project

Apprenticeship Scheduling: Learning to Schedule From Human Experts

Robot scheduling via learning from demonstration for tasks in medicine and more

Leads

Research Areas

Impact Areas

Project

Robust Learning for Autonomous Vehicles

Our goal is to create a theoretical framework and effective machine learning algorithms for robust, reliable control of autonomous vehicles. Key threads include developing metrics of confidence; and designing deep learning algorithms for parallel autonomy.

Daniela Rus

Alexander Amini

Leads

Daniela Rus

Alexander Amini

Research Areas

Impact Areas

Daniela Rus

Alexander Amini

Project

Predicting Human Motion for Fluid Human-Robot Teaming

Our project focuses on developing a general human motion prediction framework that can be applied in a variety of domains, ranging from manufacturing to space robotics, in order to improve the safety and efficiency of human-robot interaction.

Leads

Research Areas

Project

Controlling drones and other robots with gestures

Interact with robots and other devices by gesturing, using wearable muscle and motion sensors

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Computer-Aided Programming

Joseph DelPreto

Project

An Interlock for Self Driving Cars

Software Design Group

Vertical AI Community of Research

Human-Computer Interaction Community of Research

Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.

Daniel Jackson

Armando Solar-Lezama

Leads

Daniel Jackson

Armando Solar-Lezama

Research Areas

Impact Areas

Robust Learning for Autonomous Vehicles

Daniel Jackson

Armando Solar-Lezama

18 People Results matching all criteria

Alexander Amini

Graduate Student

PhD Candidate. Deep Learning; Robotics

E Amini@mit.edu

Lead

Member

PhD Candidate. Deep Learning; Robotics

Brandon Araki

Graduate Student

E araki@csail.mit.edu

Noam Buckman

Graduate Student

PhD Candidate. Autonomous vehicles. Multi-robot systems.

E nbuckman@mit.edu

Member

Model-Based Embedded and Robotics Systems Group

PhD Candidate. Autonomous vehicles. Multi-robot systems.

Richard Camilli

Research Affiliate

E romeo@mit.edu

Member

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

Member

HCI Engineering Group

Kevin Doherty

Graduate Student

E KDoherty@mit.edu

RM 32-235B

Clement Gehring

Graduate Student

E gehring@csail.mit.edu

Lead

Learning and Planning with Compressed Linear Expectation Models

Member

Ramin Hasani

Postdoctoral Associate

Machine learning Scientist

E rhasani@csail.mit.edu

Machine learning Scientist

PI
Core/Dual

Berthold Horn

Professor

Vision, computational imaging

E bkph@csail.mit.edu

T 253-5863

Lead

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Member

Visual Computing Community of Research

Vision, computational imaging

PI
Core/Dual

Leslie Kaelbling

Professor

Robotics, machine learning, motion planning

E lpk@csail.mit.edu

T 258-9695

Lead

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

Member

Robotics, machine learning, motion planning

PI
Core/Dual

John Leonard

Professor

Robotics, navigation/mapping, self-driving cars

E jleonard@csail.mit.edu

T 253-5305

Lead

Marine Robotics Group

Member

Safety Standards for Autonomous Vehicles

Robotics, navigation/mapping, self-driving cars

Lucas Liebenwein

Graduate Student

Student and Researcher | Robotics, AI | Advanced Autonomy

E lucasl@mit.edu

RM 32-376C

Lead

Member

Coresets for Machine Learning Algorithms

Student and Researcher | Robotics, AI | Advanced Autonomy

PI
Core/Dual

Tomas Lozano-Perez

Professor

Robotics, artificial intelligence

E tlp@csail.mit.edu

T 253-7889

Lead

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

Member

Robotics, artificial intelligence

PI
Core/Dual

Daniela Rus

Director

CSAIL director, robotics, AI, data science, privacy

E rus@csail.mit.edu

T 258-7567

RM 32/ 32-368/ 374

Lead

Social Network Extraction from GPS Datasets with Coresets

Safety Standards for Autonomous Vehicles

Soft Contact Modeling

Practical Secure Computation

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

Controlling drones and other robots with gestures

Using Muscle Signals to Lift Objects with Robots

Helping robots learn using demonstrations

CSAIL director, robotics, AI, data science, privacy

Wilko Schwarting

Graduate Student

E wilkos@mit.edu

Lead

Safety Standards for Autonomous Vehicles

Planning in Congestion for Autonomous Vehicles

Socially-Compliant Behavior for Autonomous Driving

Member

PI
Core/Dual

Julie Shah

Associate Professor

Robotics, machine learning, artificial intelligence

E julie_a_shah@csail.mit.edu

T 324-4879

Lead

Robots that collaborate with humans to form team plans

Robots that Quickly Adjust Team Plans to Disturbance

Human-Guided Transfer for Robot Learning

Efficient Communication for Human-Machine Teams

Member

Apprenticeship Scheduling: Learning to Schedule From Human Experts

Model-Based Embedded and Robotics Systems Group

Robotics, machine learning, artificial intelligence

Yunsheng Tian

Graduate Student

E yunsheng@mit.edu

PI
Core/Dual

Brian Williams

Professor

Robotics, autonomy, artificial intelligence, machine learning

E williams@csail.mit.edu

T 253-2739

Lead

RADMAX

Geordi: A Driver's Assistant for Risk-bounded Maneuvering

Robots Adapting to Human Intent

Risk-aware Network Resource Allocation

Uhura: Personal Assistant that Manages Risk

Artificial Social Intelligence for Successful Teams (ASIST)

Member

Robotics, autonomy, artificial intelligence, machine learning

30 News Results matching all criteria

A neural network learns when it should not be trusted

AI & ML Robotics Transportation

November 24 '20

A neural network learns when it should not be trusted

A faster way to estimate uncertainty in AI-assisted decision-making could lead to safer outcomes.

A neural network learns when it should not be trusted

AI & ML Robotics

June 30 '20

Lozano-Perez wins 2021 IEEE Robotics and Automation Award

This week it was announced that MIT professor and CSAIL principal investigator Tomas Lozano-Perez has been awarded the 2021 IEEE Robotics and Automation Award for his “foundational contributions to robot motion planning and visionary leadership in the field.”

Lozano-Perez wins 2021 IEEE Robotics and Automation Award

AI & ML Robotics

April 01 '20

Teaching robots workplace etiquette

MIT framework goes past the "how" of communication to teach robots the "if" and "when"

Teaching robots workplace etiquette

Algorithms & Theory AI & ML Robotics

March 05 '20

Showing robots how to do your chores

By observing humans, robots learn to perform complex tasks, such as setting a table.

Showing robots how to do your chores

AI & ML Robotics

February 24 '20

To self-drive in the snow, look under the road

Weather’s a problem for autonomous cars. But MIT’s new system shows promise by using “ground-penetrating radar” instead of cameras or lasers

To self-drive in the snow, look under the road

Algorithms & Theory AI & ML Robotics

February 12 '20

“Sensorized” skin helps soft robots find their bearings

Flexible sensors and an artificial intelligence model tell deformable robots how their bodies are positioned in a 3D environment.

“Sensorized” skin helps soft robots find their bearings

AI & ML Robotics Cybersecurity Internet of Things Transportation

January 13 '20

WaPo op-ed: how to regulate AI properly

In a Washington Post op-ed, CSAIL's R. David Edelman outlines how to regulate AI properly

WaPo op-ed: how to regulate AI properly

Algorithms & Theory AI & ML Robotics

November 13 '19

Autonomous system improves environmental sampling at sea

Robotic boats could more rapidly locate the most valuable sampling spots in uncharted waters.

Autonomous system improves environmental sampling at sea

AI & ML Robotics Manufacturing Transportation

October 30 '19

Self-transforming robot blocks jump, spin, flip, and identify each other

Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, a team of robots can self-assemble to form different structures with applications in inspection, disaster response, and manufacturing

Self-transforming robot blocks jump, spin, flip, and identify each other

Algorithms & Theory AI & ML Robotics Transportation

August 29 '19

MIT’s fleet of autonomous boats can now shapeshift

New capabilities allow “roboats” to change configurations to form pop-up bridges, stages, and other structures.

MIT’s fleet of autonomous boats can now shapeshift

AI & ML Robotics Health Care

July 22 '19

Prime Minister Mark Rutte of the Netherlands tours MIT

Dutch delegation visits the Institute for a tour focused on computing, robotics, and health care innovation.

Prime Minister Mark Rutte of the Netherlands tours MIT

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

AI & ML Robotics Manufacturing

May 22 '19

This robot helps you lift objects — by looking at your biceps

CSAIL system can mirror a user's motions and follow nonverbal commands by monitoring arm muscles.

This robot helps you lift objects — by looking at your biceps

AI & ML Robotics Energy Health Care

April 18 '19

Robots that can sort recycling

CSAIL’s "RoCycle" system uses in-hand sensors to detect if an object is paper, metal or plastic.

Robots that can sort recycling

AI & ML Robotics Health Care Manufacturing

March 14 '19

New robot hand is soft and strong

Gripper inspired by “origami magic ball” can grasp wide array of delicate and heavy objects

New robot hand is soft and strong

Algorithms & Theory AI & ML Robotics Transportation

February 01 '19

Engineers program marine robots to take calculated risks

Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.

Engineers program marine robots to take calculated risks

Algorithms & Theory AI & ML Robotics Transportation

January 25 '19

Identifying artificial intelligence “blind spots”

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

Identifying artificial intelligence “blind spots”

AI & ML Robotics Health Care Transportation

November 01 '18

Fleets of drones could aid searches for lost hikers

System allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable.

Fleets of drones could aid searches for lost hikers

Machines that learn language more like kids do 2

AI & ML Graphics & Vision Robotics

October 31 '18

Machines that learn language more like kids do

Computer model could improve human-machine interaction, provide insight into how children learn language.

Machines that learn language more like kids do

model helps robots naviagte like humans 1

Algorithms & Theory AI & ML Robotics Transportation

October 04 '18

Model helps robots navigate more like humans do

In simulations, robots move through new environments by exploring, observing, and drawing from learned experiences.

Model helps robots navigate more like humans do

Algorithms & Theory AI & ML Graphics & Vision Robotics

September 17 '18

Helping computers fill in the gaps between video frames

Machine learning system efficiently recognizes activities by observing how objects change in only a few key frames.

Helping computers fill in the gaps between video frames

Algorithms & Theory AI & ML Robotics Transportation

May 23 '18

Making driverless cars change lanes more like human drivers do

Algorithm computes “buffer zones” around autonomous vehicles and reassess them on the fly.

Making driverless cars change lanes more like human drivers do

AI & ML Robotics Manufacturing

May 14 '18

Materials that can change size and shape by twisting

Approach opens up new possibilities for robots, spacecraft, and more

Materials that can change size and shape by twisting

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

38 Group Results

Research Group

Anyscale Learning for All ALFA

Our vision is data-driven machine learning systems that advance the quality of healthcare, the understanding of cyber arms races and the delivery of online education.

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Lead

Una-May O'Reilly

Community of Research

Applied Machine Learning Community of Research

This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Lead

Una-May O'Reilly

Research Group

Broderick Lab

We work in the areas of statistics and machine learning.

Tamara Broderick

Leads

Tamara Broderick

Research Areas

Impact Areas

Lead

Tamara Broderick

Research Group

Center for Brains, Minds and Machines

Our main goal is developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding.

Tomaso Poggio

Leads

Tomaso Poggio

Research Areas

Impact Areas

Lead

Tomaso Poggio

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.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Lead

Aleksander Madry

Research Group

Clinical Decision-Making Group

We focus on furthering the application of technology and artificial intelligence in medicine and health-care.

+10

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Lead

Peter Szolovits

+10

Peter Szolovits

Research Group

Clinical Machine Learning Group

Our group is interested in using machine learning and artificial intelligence to transform health care.

David Sontag

Leads

David Sontag

Research Areas

Impact Areas

Lead

David Sontag

Community of Research

Cognitive AI Community of Research

This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.

Howard Shrobe

Leads

Howard Shrobe

Research Areas

Impact Areas

Lead

Howard Shrobe

Research Group

Computational Cognitive Science Group

We study the computational basis of human learning and inference.

Joshua Tenenbaum

Leads

Joshua Tenenbaum

Research Areas

Impact Areas

Lead

Joshua Tenenbaum

Research Group

Computational Design and Fabrication Group

Our research centers on digital manufacturing, 3D printing and computer graphics, as well as computational photography and displays, and virtual humans and robotics.

Wojciech Matusik

Leads

Wojciech Matusik

Research Areas

Impact Areas

Lead

Wojciech Matusik

Research Group

Computational Genomics Group

We develop new machine learning techniques and algorithms to model the transcriptional regulatory networks that control gene expression programs in living cells.

Dave Gifford

Leads

Dave Gifford

Research Areas

Impact Areas

Lead

Dave Gifford

Research Group

Computational Perception & Cognition

We combine methods from computer science, neuroscience and cognitive science to explain and model how perception and cognition are realized in human and machine.

Aude Oliva

Leads

Aude Oliva

Research Areas

Impact Areas

Lead

Aude Oliva

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.

Daniel Weitzner

Leads

Daniel Weitzner

Research Areas

Impact Areas

Lead

Daniel Weitzner

Research Group

Data-Driven Inference Group

We use machine learning and computer vision to improve outcomes in medicine, finance, and sports

John Guttag

Leads

John Guttag

Research Areas

Impact Areas

Lead

John Guttag

Research Group

Decentralized Information Group

We are investigating decentralized technologies that affect social change.

Lalana Kagal

Tim Berners-Lee

Leads

Lalana Kagal

Tim Berners-Lee

Research Areas

Impact Areas

Lead

Lalana Kagal

Tim Berners-Lee

Lalana Kagal

Tim Berners-Lee

Research Group

Distributed Robotics Laboratory

We aim to develop the science of autonomy toward a future with robots and AI systems integrated into everyday life, supporting people with cognitive and physical tasks.

+13

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

+13

Daniela Rus

Community of Research

Embodied Intelligence Community of Research

Our goal is to understand the nature of intelligent behavior in the physical world, through the study of human intelligence and the design and implementation of intelligent robots.

+12

Leslie Kaelbling

Leads

Leslie Kaelbling

Research Areas

Lead

Leslie Kaelbling

+12

Leslie Kaelbling

Research Group

Genesis Group

The Genesis Group focuses on developing an account of human intelligence with special
emphasis on developing computational models of how people tell, perceive and understand
stories.

Leads

Research Areas

Impact Areas

Research Group

Geometric Data Processing Group

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

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Lead

Justin Solomon

Research Group

Imagination, Computation, and Expression Laboratory (ICE Lab)

We develop computing systems for creative expression, cultural analysis, and social change.

Peter Mawhorter

D. Fox Harrell

Leads

D. Fox Harrell

Research Areas

Impact Areas

Lead

D. Fox Harrell

Peter Mawhorter

D. Fox Harrell

Research Group

Infolab

We investigate language in different contexts: from how it is learned, to how it is grounded in visual perception, all the way to how machines can readily interact with humans.

Boris Katz

Leads

Boris Katz

Research Areas

Impact Areas

Lead

Boris Katz

Research Group

Interactive Robotics Group

The Interactive Robotics Group aims to imagine the future of work by designing collaborative robot teammates that enhance human capability.

Julie Shah

Leads

Julie Shah

Research Areas

Impact Areas

Lead

Julie Shah

Research Center

Internet Policy Research Initiative

Our mission is to work with policy makers and cybersecurity technologists to increase the trustworthiness and effectiveness of interconnected digital systems.

Leads

Daniel Weitzner

Hal Abelson

David Clark

Lalana Kagal

Karen Sollins

Gerald Sussman

Research Areas

Impact Areas

Lead

Daniel Weitzner

Hal Abelson

David Clark

Lalana Kagal

Karen Sollins

Gerald Sussman

Research Group

Learning and Intelligent Systems

We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Lead

Leslie Kaelbling

Tomas Lozano-Perez

Leslie Kaelbling

Tomas Lozano-Perez

115 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.

Leads

Research Areas

Impact Areas

Computational Design and Fabrication Group

Project

3D-Printed Organic Solar Cells

Using our inkjet printer system, the goal of this project is to produce a light sensor / solar cell that is composed entirely of 3D printed materials.

James Minor

Leads

James Minor

Research Areas

Impact Areas

James Minor

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.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Anyscale Learning for All ALFA

Leslie Kaelbling

Tomas Lozano-Perez

Project

Adversarial Cyber Security

Our goal is to understand the nature of cyber security arms races between malicious and bonafide parties. Our vision is autonomous cyber defenses that anticipate and take measures against counter attacks.

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

Impact Areas

Computer-Aided Programming

Una-May O'Reilly

Project

An Interlock for Self Driving Cars

Software Design Group

Vertical AI Community of Research

Human-Computer Interaction Community of Research

Daniel Jackson

Armando Solar-Lezama

Leads

Daniel Jackson

Armando Solar-Lezama

Research Areas

Impact Areas

Daniel Jackson

Armando Solar-Lezama

Project

Apprenticeship Scheduling: Learning to Schedule From Human Experts

Robot scheduling via learning from demonstration for tasks in medicine and more

Leads

Research Areas

Impact Areas

Spoken Language Systems Group

Project

Arabic Language Processing

The Arabic language is spoken by over one billion people around the world. Arabic presents a variety of challenges for speech and language processing technologies. In our group, we have several research topics examining Arabic, including dialect identification, speech recognition, machine translation, and language processing.

Mitra Mohtarami

Leads

Mitra Mohtarami

Research Areas

Computation Structures Group

Mitra Mohtarami

Project

Artificial Social Intelligence for Successful Teams (ASIST)

Computational Cognitive Science Group

Model-Based Embedded and Robotics Systems Group

Our goal is to develop a socially intelligent team coacher agent that helps humans communicate, strategize, and work together efficiently.

Leads

Howard Shrobe

Brian Williams

Boris Katz

Zhutian Yang

Research Areas

Computer Architecture

Impact Areas

Weiss Laboratory for Synthetic Biology

Wireless

Project

Artificial tissue homeostasis

In order to be able to design synthetic organs that function autonomously, we will need to engineer artificial tissue homeostasis. To control the size of these artificial tissues, two major mechanisms will have to be engineered.

Leads

Research Areas

Impact Areas

Natural Language Processing Group

Project

Aspect-Augmented Adversarial Networks for Domain Adaptation

Vertical AI Community of Research

We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.

Regina Barzilay

Tommi Jaakkola

Leads

Regina Barzilay

Tommi Jaakkola

Research Areas

Spoken Language Systems Group

Regina Barzilay

Tommi Jaakkola

Project

Automatic Speech Recognition

Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.

Sameer Khurana

Jim Glass

Leads

Sameer Khurana

Jim Glass

Research Areas

Impact Areas

Sameer Khurana

Jim Glass

Project

Autonomous Vehicle Databases

Data Systems Group

The impending success of AV technology will create vehicles that collect sensor data at a high rate.

Michael Stonebraker

Sam Madden

Leads

Michael Stonebraker

Sam Madden

Research Areas

Impact Areas

Clinical Decision-Making Group

Michael Stonebraker

Sam Madden

Project

AWE-CM Vectors: Augmenting Word Embeddings with a Clinical Metathesaurus

Adding domain knowledge to word embeddings.

Peter Szolovits

Leads

Peter Szolovits

Research Areas

Impact Areas

Peter Szolovits

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.

Leads

Research Areas

Impact Areas

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.

Leads

Research Areas

Impact Areas

Project

Blood Pressure Imager

Development of affordable wearable continuous blood pressure monitor based on radial arterial pulse imaged from the skin surface of human wrist

Mumin Jin

Aya G. Halawi

Jianhua Li

Leads

Abhijit Biswas

Ted Adelson

Mandayam A. Srinivasan

Mohan Thanikachalam

Research Areas

Mumin Jin

Aya G. Halawi

Jianhua Li

Project

Brain-Controlled Robots

This project aims to let people correct robot mistakes with nothing more than their brain signals - to allow robots to adapt to humans rather than the other way around

Frank H. Guenther

Daniela Rus

Joseph DelPreto

Leads

Daniela Rus

Joseph DelPreto

Research Areas

Decentralized Information Group

Frank H. Guenther

Daniela Rus

Joseph DelPreto

Project

The Car Can Explain!

Computing & Society Community of Research

Developing techniques to allow self-driving cars and other AI-driven systems to explain behaviors and failures.

Gerald Sussman

Lalana Kagal

Leads

Gerald Sussman

Lalana Kagal

Research Areas

Impact Areas

Gerald Sussman

Lalana Kagal

Project

Cardiac MRI analysis

Medical Vision Group

We develop image analysis algorithms for whole-heart cardiac MRI in patients with severe congenital heart disease.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

Project

CliNER: Clinical Concept Extraction

Clinical concept extraction (CCE) of named entities - such as problems, tests, and treatments - aids in forming an understanding of notes and provides a foundation for many downstream clinical decision-making tasks. Historically, this task has been posed as a standard named entity recognition (NER) sequence tagging problem, and solved with feature-based methods using hand-engineered domain knowledge. Recent advances, however, have demonstrated the efficacy of LSTM-based models for NER tasks, including CCE. This work presents CliNER 2.0, a simple-to-install, open-source tool for extracting concepts from clinical text. CliNER 2.0 uses a word- and character- level LSTM model, and achieves state-of-the-art performance. For ease of use, the tool also includes pre-trained models available for public use.

Leads

William Boag

Elena Sergeeva

Peter Szolovits

Anna Rumshisky

Research Areas

Impact Areas

Project

Clinical Intervention Prediction with Neural Networks

Integrating multi-modal clinical data and using recurrent and convolution neural networks to predict when patients will need important interventions.

Leads

Research Areas

Impact Areas

Project

Clinical Neuroimaging

Medical Vision Group

We develop novel algorithms for neuroimage segmentation, registration, and joint modeling of brain structure with clinical and genetic data.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas