Research

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

Spoken Language Systems Group

Our goal is to create technology that makes it possible for everyone in the world to interact with with computers via natural spoken language.

Jim Glass

Leads

Jim Glass

Research Areas

Impact Areas

Lead

Jim Glass

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 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

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

Decentralized Information Group

Impact Areas

Lead

Daniel Weitzner

9 Project Results matching all criteria

Project

The Car Can Explain!

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

Learning and Intelligent Systems

Gerald Sussman

Lalana Kagal

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

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

Mitra Mohtarami

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

Unsupervised Speech Processing

All humans process vast quantities of unannotated speech and manage to learn phonetic inventories, word boundaries, etc., and can use these abilities to acquire new word. Why can't ASR technology have similar capabilities? Our goal in this research project is to build speech technology using unannotated speech corpora.

Yu-An Chung

Sameer Khurana

Leads

Yu-An Chung

Sameer Khurana

Research Areas

Impact Areas

Yu-An Chung

Sameer Khurana

Project

START System

Infolab

The START Natural Language System understands and generates language and answers questions that are posed to it in natural language.

Boris Katz

Sue Felshin

Leads

Boris Katz

Sue Felshin

Research Areas

Boris Katz

Sue Felshin

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

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

Daniel Jackson

Armando Solar-Lezama

16 People Results matching all criteria

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

Member

HCI Engineering Group

Sue Felshin

Research Scientist

E sfelshin@csail.mit.edu

T 253-2965

Lead

START System

Member

Infolab

PI
Core/Dual

Jim Glass

Senior Research Scientist

speech and language processing

E glass@mit.edu

T 253-1640

Lead

Designing Information-Rich Embedding Spaces

Unsupervised Learning of Interpretable Representations from Sequential Data

Fact-Checking and Reasoning

Speaker Verification and Diarization

Automatic Speech Recognition

Language and Dialect Identification

Member

Using Computers to Eat Healthier

Low-Power Speech Processing Circuits

Crossing the Vision-Language Boundary

Unsupervised Speech Processing

Arabic Language Processing

Health-related Biomarkers from Speech

Community Question Answering

Understanding Language Representations in Deep Learning Models

Embodied Intelligence Community of Research

speech and language processing

Lelia Hampton

Graduate Student

Ph.D. Student

E lelia@mit.edu

Ph.D. Student

PI
Core/Dual

David Karger

Professor

Helping people manage information

E karger@csail.mit.edu

T 258-6167

Lead

Haystack Group

Mavo: Creating Interactive Data-Driven Web Applications by Authoring HTML

Algorithms Group

Wikum: Bridging Discussion Systems and Wikis with Collective Summarization

Squadbox: Combating Online Harassment using Friendsourced Moderation

Eyebrowse: Social and Public Web Browsing

Talkademic: Motivating Online Academic Discourse

Member

Theory of Computation Community of Research

Systems Community of Research

Helping people manage information

Alexandre Kaspar

Graduate Student

Graphics, crowdsourcing and hybrid fabrication.

E akaspar@mit.edu

Lead

Crowdsourcing in Graphics and Vision

Computational Knitting: Patterning and Shaping for Everyone

Graphics, crowdsourcing and hybrid fabrication.

PI
Core/Dual

Boris Katz

Principal Research Scientist

Natural language processing

E boris@csail.mit.edu

T 253-6032

Lead

Infolab

START System

Semantic Parsing using Vision

Generating Plans for Agents using Video and Language

Inferring “Theory of Mind” with vision and language

Artificial Social Intelligence for Successful Teams (ASIST)

Member

Center for Brains, Minds and Machines

Automatic Speech Recognition

Natural language processing

Sameer Khurana

Graduate Student

unsupervised speech processing, automatic speech recognition

E skhurana@mit.edu

Lead

Unsupervised Speech Processing

Language and Dialect Identification

Member

Internet Policy Research Initiative

unsupervised speech processing, automatic speech recognition

Kimberle Koile

Lecturer

E kkoile@csail.mit.edu

Henry Lieberman

Research Scientist

Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.

E lieber@MEDIA.MIT.EDU

Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.

Jamie Macbeth

Research Affiliate

In-Depth Natural Language Understanding, Human-Computer Interaction

E jmacbeth@mit.edu

In-Depth Natural Language Understanding, Human-Computer Interaction

Nouran Soliman

Graduate Student

Nouran is a PhD Student in EECS at MIT working with David Karger.

E nouran@mit.edu

Nouran is a PhD Student in EECS at MIT working with David Karger.

Dishita Turakhia

Graduate Student

PhD student in HCIE group at CSAIL

E dishita@mit.edu

Member

HCI Engineering Group

PhD student in HCIE group at CSAIL

PI
Core/Dual

Daniel Weitzner

Principal Research Scientist

Web policy, cybersecurity, web architecture

E djweitzner@csail.mit.edu

T 253-8036

Lead

Keys Under Doormats

Member

Data Systems Group

Web policy, cybersecurity, web architecture

Junyi Zhu

Graduate Student

E junyizhu@mit.edu

Member

HCI Engineering Group

PI
Core/Dual

Victor Zue

Professor

Spoken language systems, human language technologies

E zue@csail.mit.edu

T 253-8513

Member

Spoken language systems, human language technologies

10 News Results matching all criteria

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 Human-Computer Interaction Big Data Education Entertainment Health Care Internet of Things

December 11 '17

Reading a neural network’s mind

Neural networks, which learn to perform computational tasks by analyzing huge sets of training data, have been responsible for the most impressive recent advances in artificial intelligence, including speech-recognition and automatic-translation systems.

Reading a neural network’s mind

PhD student Danielle Olson believes we can make digital information-sharing more natural and interpersonal, by creating immersive media to better understand each other’s feelings and backgrounds.

AI & ML Human-Computer Interaction Entertainment

May 30 '17

Danielle Olson: Building empathy through computer science and art

Communicating through computers has become an extension of our daily reality. But as speaking via screens has become commonplace, our exchanges are losing inflection, body language, and empathy. Danielle Olson ’14, a first-year PhD student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), believes we can make digital information-sharing more natural and interpersonal, by creating immersive media to better understand each other’s feelings and backgrounds.

Danielle Olson: Building empathy through computer science and art

MIT doctoral candidate Claudia Pérez-D'Arpino discusses her work teaching the Optimus robot to perform various tasks, including picking up a bottle.

AI & ML Human-Computer Interaction Robotics Manufacturing

May 10 '17

Teaching robots to teach other robots

Most robots are programmed using one of two methods: learning from demonstration, in which they watch a task being done and then replicate it, or via motion-planning techniques such as optimization or sampling, which require a programmer to explicitly specify a task’s goals and constraints.

Teaching robots to teach other robots

WaitSuite opens up new possibilities for "micro-learning," leveraging idle moments like when you're waiting to connect to WiFi or refresh your email.

AI & ML Human-Computer Interaction Education Internet of Things

April 18 '17

Learn a language while you wait for WiFi

Hyper-connectivity has changed the way we communicate, wait, and productively use our time. Even in a world of 5G wireless and “instant” messaging, there are countless moments throughout the day when we’re waiting for messages, texts, and Snapchats to refresh. But our frustrations with waiting a few extra seconds for our emails to push through doesn’t mean we have to simply stand by.

Learn a language while you wait for WiFi

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 & ML Human-Computer Interaction Energy Internet of Things Manufacturing

February 13 '17

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

MIT researchers have developed a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings.

AI & ML Human-Computer Interaction Big Data

December 06 '16

Learning words from pictures

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

Learning words from pictures

A prototype of a new speech-controlled nutrition-logging system allows users to verbally describe the contents of a meal. The system then parses the description and automatically retrieves the pertinent nutritional data.

AI & ML Human-Computer Interaction Health Care

March 24 '16

Voice-controlled calorie counter

For people struggling with obesity, logging calorie counts and other nutritional information at every meal is a proven way to lose weight. The technique does require consistency and accuracy, however, and when it fails, it’s usually because people don't have the time to find and record all the information they need.A few years ago, a team of nutritionists from Tufts University who had been experimenting with mobile-phone apps for recording caloric intake approached members of the Spoken Language Systems Group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), with the idea of a spoken-language application that would make meal logging even easier.

Voice-controlled calorie counter

System that learns to distinguish words’ phonemes could aid development of speech-processing systems for under-studied languages

AI & ML Human-Computer Interaction Big Data Education Entertainment Internet of Things

September 14 '15

Learning spoken language

Every language has its own collection of phonemes, or the basic phonetic units from which spoken words are composed. Depending on how you count, English has somewhere between 35 and 45. Knowing a language’s phonemes can make it much easier for automated systems to learn to interpret speech.In the 2015 volume of Transactions of the Association for Computational Linguistics, CSAIL researchers describe a new machine-learning system that, like several systems before it, can learn to distinguish spoken words. But unlike its predecessors, it can also learn to distinguish lower-level phonetic units, such as syllables and phonemes.

Learning spoken language

AI & ML Human-Computer Interaction

October 24 '13

Automatic speaker tracking in audio recordings

CSAIL’s Spoken Language Systems Group has unveiled a new technique for automatically tracking speakers in audio recordings. The new technique tackles the task of speaker diarization, or computationally determining how many speakers are present in a recording.

Automatic speaker tracking in audio recordings

40 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 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

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.

+11

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

+11

Daniela Rus

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

Haystack Group

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

Lea Verou

David Karger

Leads

David Karger

Research Areas

Impact Areas

Internet of Things

Lead

David Karger

Lea Verou

David Karger

Research Group

HCI Engineering Group

We work at the intersection of human computer interaction and personal fabrication tools.

Stefanie Mueller

Leads

Stefanie Mueller

Research Areas

Impact Areas

Lead

Stefanie Mueller

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.

Stefanie Mueller

Leads

Stefanie Mueller

Research Areas

Impact Areas

Lead

Stefanie Mueller

Research Group

Human-Computer Interaction

HCI faculty and researchers in CSAIL.

Leads

Randall Davis

Fredo Durand

Daniel Jackson

David Karger

Rob Miller

Stefanie Mueller

Research Areas

Lead

Randall Davis

Fredo Durand

Daniel Jackson

David Karger

Rob Miller

Stefanie Mueller

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 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

105 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

Learning and Intelligent Systems

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

Imagination, Computation, and Expression Laboratory (ICE Lab)

Una-May O'Reilly

Project

AIRvatar System: A Framework for Analyzing Virtual Identities

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

Impact Areas

D. Fox Harrell

Project

Alloy

Software Design Group

Alloy is a language for describing structures and a tool for exploring them. It has been used in a wide range of applications from finding holes in security mechanisms to designing telephone switching networks. Hundreds of projects have used Alloy for design analysis, for verification, for simulation, and as a backend for many other kinds of analysis and synthesis tools, and Alloy is currently being taught in courses worldwide.

Daniel Jackson

Leads

Daniel Jackson

Research Areas

Computer-Aided Programming

Impact Areas

Daniel Jackson

Project

An Interlock for Self Driving Cars

Software Design Group

Daniel Jackson

Armando Solar-Lezama

Leads

Daniel Jackson

Armando Solar-Lezama

Research Areas

Impact Areas

Interactive Robotics Group

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

Project

Arabic 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

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

Regina Barzilay

Tommi Jaakkola

Project

Automatic Speech Recognition

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

Learning and Intelligent Systems

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

Energy

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!

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

Imagination, Computation, and Expression Laboratory (ICE Lab)

Polina Golland

Project

Changing Minds via Roleplay in Interactive Digital Media

We aim to study the impact of computer-supported roleplaying in changing social perspectives of digital media users. Such media could take the form of videogames, VR systems, training software, and other types of interactive narrative technology.

Pablo Jose Ortiz-Lampier

D. Fox Harrell

Leads

Pablo Jose Ortiz-Lampier

D. Fox Harrell

Research Areas

Impact Areas

Pablo Jose Ortiz-Lampier

D. Fox Harrell

Project

CilkPride

Usable Programming Group

CilkPride is a programming environment that aims to make performance and safety information always available and appropriately visible to the programmer.

Charles E. Leiserson

Rob Miller

Leads

Charles E. Leiserson

Rob Miller

Research Areas

Charles E. Leiserson

Rob Miller

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

Peter Szolovits

Anna Rumshisky

Research Areas

Impact Areas

Energy

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

Polina Golland

115 People Results

Murad Abu-Khalaf

Program Manager, Toyota-CSAIL Joint Research Center

E murad@mit.edu

T 452-2859

PI
Core/Dual

Ted Adelson

Professor

Vision, touch sensing, robotics

E adelson@csail.mit.edu

T 253-0645

Lead

Embodied Intelligence Community of Research

Blood Pressure Imager

Member

Visual Computing Community of Research

Vision, touch sensing, robotics

Monica Agrawal

Graduate Student

E magrawal@mit.edu

Leandro Agudelo

Research Scientist

Systems Genomics, CompBio, Topological Integrative Biology, Biology

E lagudelo@csail.mit.edu

Systems Genomics, CompBio, Topological Integrative Biology, Biology

Ekin Akyürek

Graduate Student

First year PhD student in computer science

E akyurek@mit.edu

First year PhD student in computer science

Alexander Amini

Graduate Student

PhD Candidate. Deep Learning; Robotics

E Amini@mit.edu

Lead

Robust Learning for Autonomous Vehicles

Member

Self-Driving Vehicles

Natural Language Processing Group

PhD Candidate. Deep Learning; Robotics

Brandon Araki

Graduate Student

E araki@csail.mit.edu

Anish Athalye

Graduate Student

PhD student in PDOS

E aathalye@mit.edu

RM 32-G978A

PhD student in PDOS

PI
Core/Dual

Regina Barzilay

Professor

Natural language processing, machine learning

E regina@csail.mit.edu

T 258-5706

Lead

Aspect-Augmented Adversarial Networks for Domain Adaptation

Member

Natural language processing, machine learning

Cenk Baykal

Graduate Student

E baykal@mit.edu

RM 32-376C

Member

Coresets for Machine Learning Algorithms

Laurent Bindschaedler

Postdoctoral Fellow

Systems for Big Data and ML. ML for Systems.

E bindscha@mit.edu

Member

Data Systems Group

Systems for Big Data and ML. ML for Systems.

William Boag

Graduate Student

Clinical NLP

E wboag@mit.edu

Lead

Synthetically-Identified Clinical Notes

Information Retrieval for Cancer Treatments in Clinical Literature and Trial Eligibility

CliNER: Clinical Concept Extraction

Quantifying Racial Disparities in End-of-Life Care

Member

Clinical Decision-Making Group

Clinical NLP

Laura E. Brandt

Graduate Student

E lebrandt@csail.mit.edu

PI
Core/Dual

Tamara Broderick

Associate Professor

Machine learning, statistics, Bayesian methods, inference

E tbroderick@csail.mit.edu

T 324-6749

Lead

Scalable Bayesian Inference via Adaptive Data Summaries

Scalable Bayesian inference with optimization

Broderick Lab

Member

Machine learning, statistics, Bayesian methods, inference

Noam Buckman

Graduate Student

PhD Candidate. Autonomous vehicles. Multi-robot systems.

E nbuckman@mit.edu

Member

Model-Based Embedded and Robotics Systems Group

Self-Driving Vehicles

PhD Candidate. Autonomous vehicles. Multi-robot systems.

Richard Camilli

Research Affiliate

E romeo@mit.edu

Member

Geeticka Chauhan

Graduate Student

E geeticka@mit.edu

Member

Clinical Decision-Making Group

Quantification of Pulmonary Edema in Chest Radiographs

Irene Chen

Graduate Student

Machine learning, healthcare, fairness

E iychen@mit.edu

Member

Clinical Machine Learning Group

Unsupervised Speech Processing

Machine learning, healthcare, fairness

Yu-An Chung

Graduate Student

Speech and language processing, machine learning

E andyyuan@mit.edu

Lead

Member

Adversarial Cyber Security

Speech and language processing, machine learning

Valentin Churavy

Graduate Student

#JuliaLang, #GPU, #HPC

E vchuravy@MIT.EDU

Member

Julia Lab

#JuliaLang, #GPU, #HPC

Adrian Dalca

Research Scientist

E adalca@csail.mit.edu

Member

Medical Vision Group

Clinical Neuroimaging

PI
Core/Dual

Costis Daskalakis

Associate Professor

Algorithms, Economics, Game Theory, Statistics, Machine Learning

E costis@csail.mit.edu

T 253-9643

Lead

Algorithms Group

Is the Casino using a Riffle Shuffle?

Understanding Convergence of Iterative Algorithms

Theory of Computation Community of Research

Member

Center for Deployable Machine Learning (CDML)

Algorithms, Economics, Game Theory, Statistics, Machine Learning

PI
Core/Dual

Randall Davis

Professor

Human-computer interaction, systems, classic AI

E davis@csail.mit.edu

T 253-5879

Lead

Multimodal Understanding Group

Member

Supervising Robots with Muscle and Brain Signals

Human-computer interaction, systems, classic AI

Joseph DelPreto

Graduate Student

Works on human-robot interaction using muscle and brain signals.

E delpreto@csail.mit.edu

RM 32-376A

Lead

Brain-Controlled Robots

Distributed Robot Garden

Robot Compiler

SoFi - The Soft Robotic Fish

Soft Robotic Hand

Using Muscle Signals to Lift Objects with Robots

Controlling drones and other robots with gestures

Helping robots learn using demonstrations

Member