Research

Impact Areas

Lead

Una-May O'Reilly

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

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

Computation and Biology

Our lab focuses on designing algorithms to gain biological insights from advances in automated data collection and the subsequent large data sets drawn from them.

Bonnie Berger

Leads

Bonnie Berger

Research Areas

Impact Areas

Lead

Bonnie Berger

Research Group

Computational Biology Group

We seek to understand the mechanistic basis of human disease, using a combination of computational and experimental techniques.

Manolis Kellis

Leads

Manolis Kellis

Research Areas

Impact Areas

Lead

Manolis Kellis

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 Connectomics Group

Our group’s goal is to create, based on such microscopic connectivity and functional data, new mathematical models explaining how neural tissue computes.

Nir Shavit

Leads

Nir Shavit

Research Areas

Impact Areas

Lead

Nir Shavit

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

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

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

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

Transportation

Lead

Julie Shah

Research Group

Medical Vision Group

We develop new algorithms for medical image analysis and visualization of medical imagery.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Lead

Polina Golland

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

Energy

Internet of Things

Lead

John Fisher

Community of Research

Theory of Computation Community of Research

The goal of the Theory of Computation CoR is to study the fundamental strengths and limits of computation as well as how these interact with mathematics, computer science, and other disciplines.

+20

Costis Daskalakis

Leads

Costis Daskalakis

Research Areas

Impact Areas

Lead

Costis Daskalakis

+20

Costis Daskalakis

Research Group

Tidor Lab

We focus on the analysis of complex biological systems at both the molecular level and the systems level.

Bruce Tidor

Leads

Bruce Tidor

Research Areas

Impact Areas

Lead

Bruce Tidor

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.

Martin Rinard

Leads

Martin Rinard

Research Areas

Impact Areas

Lead

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

Impact Areas

Entertainment

Transportation

Lead

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Antonio Torralba

Research Group

Weiss Laboratory for Synthetic Biology

The Weiss Lab seeks to create integrated biological systems capable of autonomously performing useful tasks, and to elucidate the design principles underlying complex phenotypes.

Leads

Research Areas

Impact Areas

Computational Cognitive Science Group

32 Project Results

Project

A Counterfactual Simulation Model of Causal Judgment

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

Impact Areas

Learning and Intelligent Systems

Project

Active Learning of Models for Planning

Machine Learning

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

Interactive Robotics Group

Entertainment

Leslie Kaelbling

Tomas Lozano-Perez

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

Computation Structures Group

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

Transportation

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

Project

Cardiac MRI analysis

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

Clinical Intervention Prediction with Neural Networks

Applied Machine Learning Community of Research

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

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

Computational Design and Fabrication Group

Polina Golland

Project

Crowdsourcing in Graphics and Vision

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

Impact Areas

Entertainment

Alexandre Kaspar

Project

Drug Target Identification Via Computational Modeling

Tidor Lab

We use computational models of biological pathways in disease to estimate the range of possible responses to various hypothetical drugs, given patient variability and limited pathway-relevant data.

Leads

Research Areas

Impact Areas

Weiss Laboratory for Synthetic Biology

Project

Flexible cellular platforms for reconfigurable biocircuits

Existing methods for cloning and recombination of DNA enable construction of arbitrary sequences. However, the sequential nature of these techniques makes them time-consuming and expensive. Furthermore, while the transformation of an existing plasmid into a host strain can be reliable when a selection marker is used, there are many current limitations: the number of different plasmids that can be co-transformed is limited by the choice of markers and compatible origins of replication; plasmids are less stable than chromosomal DNA and are difficult to maintain indefinitely without mutation; and cistronic interactions cannot be designed since each new nucleotide sequence added is on an unconnected DNA molecule. To overcome these limitations, we are designing reconfigurable chromosomes consisting of both fixed and variable regions. While the fixed region is carefully optimized and tuned ahead of time, the variable region can be modified in the field, at the point-of-use, leading to rapid and on-demand realization of novel biocircuits with many different phenotypes.

Leads

Research Areas

Impact Areas

Project

Geometry and topology for scientific computing and shape analysis

Computer Graphics Group

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

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

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

Impact Areas

Interactive Robotics Group

Internet of Things

Justin Solomon

Stefanie Jegelka

Project

Human-Robot Collaboration in Shared Workspace

Models and algorithms to predict in real time the next actions of a human working together with a robot

Leads

Research Areas

Impact Areas

Project

Metagenomic Binning Through Low-Density Hashing

Our goal is to develop better metagenomic binning by origin species of fragments of sequenced environmental DNA.

Bonnie Berger

Leads

Bonnie Berger

Research Areas

Impact Areas

Bonnie Berger

Project

Monitoring Fetal Health

We develop algorithms for fetal MRI interpretation, to enable noninvasive fetal monitoring from MRI.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Polina Golland

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.

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

Project

Population-level Tumor Growth Inhibition Modeling

Tidor Lab

We develop, parameterize, and validate a model for tumor growth inhibition using in vivo mouse data and study the effects of modeling uncertainty and inter-individual variability on drug candidate efficacy predictions.

Leads

Research Areas

Impact Areas

Project

Quantification of Pulmonary Edema in Chest Radiographs

We develop machine learning algorithms to automatically and quantitatively assess the severity of pulmonary edema from chest x-ray images.

Polina Golland

Leads

Polina Golland

Research Areas

Impact Areas

Computational Connectomics Group

Polina Golland

Project

Reconstructing Neural Circuits from Mammalian Brain

Theory of Computation Community of Research

We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.

Yaron Meirovitch

Nir Shavit

Leads

Yaron Meirovitch

Nir Shavit

Research Areas

Impact Areas

Yaron Meirovitch

Nir Shavit

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.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Model-Based Embedded and Robotics Systems Group

Aleksander Madry

Project

Robots Adapting to Human Intent

Our goal is to develop algorithms for allowing robots to adapt intelligently to humans when they are working together as a team.

Brian Williams

Leads

Brian Williams

Research Areas

Clinical Decision-Making Group

Impact Areas

Brian Williams

Project

Semi-Supervised Regression with Cycle Wasserstein Regression GANs

Using adversarial signals and a cycle-consistency based regularization, we can supplement paired regression tasks with unpaired data to improve regression performance.

Leads

Research Areas

Impact Areas

Project

Sensible Deep Learning for 3D Data

Machine Learning

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

Justin Solomon

Leads

Justin Solomon

Research Areas

Impact Areas

Justin Solomon

37 People Results

Mazdak Abulnaga

Graduate Student

medical vision

E abulnaga@mit.edu

Member

Monitoring Fetal Health

Geometry and topology for scientific computing and shape analysis

medical vision

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

Robust Learning for Autonomous Vehicles

Vision, touch sensing, robotics

Leandro Agudelo

Research Scientist

Systems Genomics, CompBio, Topological Integrative Biology, Biology

E lagudelo@csail.mit.edu

Systems Genomics, CompBio, Topological Integrative Biology, Biology

Alexander Amini

Graduate Student

PhD Candidate. Deep Learning; Robotics

E Amini@mit.edu

Lead

Member

Self-Driving Vehicles

Distributed Robotics Laboratory

PhD Candidate. Deep Learning; Robotics

Brandon Araki

Graduate Student

E araki@csail.mit.edu

PI
Core/Dual

Regina Barzilay

Professor

Natural language processing, machine learning

E regina@csail.mit.edu

T 258-5706

Lead

Natural Language Processing Group

Aspect-Augmented Adversarial Networks for Domain Adaptation

Member

Vertical AI Community of Research

Applied Machine Learning Community of Research

Natural language processing, machine learning

PI
Core/Dual

Bonnie Berger

Professor

Computational biology, genomics

E bab@csail.mit.edu

T 253-1827

Lead

Covering All K-mers Using Joker Characters

Towards Context-Aware Functional Genomics

Metagenomic Binning Through Low-Density Hashing

Computation and Biology

Member

Computation Structures Group

Theory of Computation Community of Research

Applied Machine Learning Community of Research

Computational biology, genomics

Lillian Chin

Graduate Student

E ltchin@mit.edu

Lead

Sensorized Soft Electric Hands with Handed Shearing Auxetics

Member

Distributed Robotics Laboratory

Yu-An Chung

Graduate Student

Speech and language processing, machine learning

E andyyuan@mit.edu

Lead

Unsupervised Speech Processing

Member

Spoken Language Systems Group

Speech and language processing, machine learning

Azim Dehghani Amirabad

Postdoctoral Associate

Machine learning, Bioinformatics

E azimd@csail.mit.edu

Machine learning, Bioinformatics

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

Member

HCI Engineering Group

PI
Core/Dual

Fredo Durand

Professor

Vision, graphics, computational photography

E fredo@csail.mit.edu

T 253-7223

Lead

Computer Graphics Group

Computational Understanding of Visualizations and Graphic Designs

Member

Computational Bounce Flash for Indoor Portraits

Halide

Vision, graphics, computational photography

Barış Ekim

Graduate Student

E ekim@csail.mit.edu

PI
Core/Dual

John Fisher

Senior Research Scientist

Vision, machine learning, signal processing

E fisher@csail.mit.edu

T 253-0788

Lead

Sensing, Learning and Inference

Member

Vision, machine learning, signal processing

PI
Core/Dual

Polina Golland

Professor

Vision, medical imaging, statistical inference

E polina@csail.mit.edu

T 253-8005

Lead

Quantification of Pulmonary Edema in Chest Radiographs

Monitoring Fetal Health

Cardiac MRI analysis

Clinical Neuroimaging

Member

Data-Driven Inference Group

Vision, medical imaging, statistical inference

PI
Core/Dual

John Guttag

Professor

Machine learning, medicine, data-driven inference

E guttag@csail.mit.edu

T 253-6022

Lead

Hidden Influencers, Risk and Causes of Infection

Predicting Adverse Events Across Changing Electronic Health Record Systems

Machine Learning for Insights in Basketball Strategy

Detecting Voice Misuse to Diagnose Disorders

Member

Systems Community of Research

Machine learning, medicine, data-driven inference

Ramin Hasani

Postdoctoral Associate

Machine learning Scientist

E rhasani@csail.mit.edu

Machine learning Scientist

Evan Hernandez

Graduate Student

Natural language processing, machine learning, interpretability

E dez@mit.edu

Natural language processing, machine learning, interpretability

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

Konstantin Krismer

Graduate Student

E krismer@mit.edu

Member

Computational Genomics Group

PI
Core/Dual

John Leonard

Professor

Robotics, navigation/mapping, self-driving cars

E jleonard@csail.mit.edu

T 253-5305

Lead

Marine Robotics Group

Self-Driving Vehicles

Member

Embodied Intelligence Community of Research

Robotics, navigation/mapping, self-driving cars

Ruizhi Liao

Graduate Student

E ruizhi@mit.edu

Member

Monitoring Fetal Health

Quantification of Pulmonary Edema in Chest Radiographs

Lucas Liebenwein

Graduate Student

Student and Researcher | Robotics, AI | Advanced Autonomy

E lucasl@mit.edu

RM 32-376C

Lead

Safety Standards for Autonomous Vehicles

Member

Self-Driving Vehicles

Distributed Robotics Laboratory

Coresets for Machine Learning Algorithms

Student and Researcher | Robotics, AI | Advanced Autonomy

Razvan Marinescu

Postdoctoral Associate

Postdoctoral Associate working with Polina Golland on neuroimaging.

E razvan@csail.mit.edu

RM 32-D475B

Member

Clinical Neuroimaging