Research

Impact Areas

Lead

Tomaso Poggio

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

Manufacturing

Lead

Peter Szolovits

+10

Peter Szolovits

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 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

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

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

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

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

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

Security & Cryptography

Impact Areas

Lead

Costis Daskalakis

+20

Costis Daskalakis

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

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

19 Project Results matching all criteria

Project

Covering All K-mers Using Joker Characters

We developed a new algorithm to generate compact sequence sets covering all k-mers using joker characters.

Bonnie Berger

Leads

Bonnie Berger

Research Areas

Bonnie Berger

Project

Towards Context-Aware Functional Genomics

We aim to develop a context-aware data-driven functional genomics framework that can characterize tissue-specific gene representations, provide context-aware genotype to phenotype mapping, and enable network-based exploration of disease genetics.

Bonnie Berger

Leads

Bonnie Berger

Research Areas

Impact Areas

Computational Connectomics Group

Bonnie Berger

Project

Reconstructing Neural Circuits from Mammalian Brain

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

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

Population-level Tumor Growth Inhibition Modeling

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

Fast & Accurate Alignment of Barcoded Short Reads

This project aims to develop an improved alignment algorithm for barcoded short reads as produced by 10X Genomics' sequencing platform.

Leads

Research Areas

Project

Structure-based Inhibitor Design Against ZIKV Protease

This project uses computational biology and chemistry tools to design a pharmaceutical to combat the Zika Virus.

Leads

Research Areas

Impact Areas

Project

Drug Target Identification Via Computational Modeling

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

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

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

Computational Biology Group

Project

Rational Design of Therapeutics

We use all-atom biophysical models to design inhibitors for hard-to-drug targets

Leads

Research Areas

Project

Modeling of Targeted Liposomal Doxorubicin Dynamics

Our goal is to develop a model for the binding, internalization, and tumor-killing dynamics of liposome-enclosed doxorubicin targeted to cancer cells and develop design principles for creating more effective therapeutics.

Rui Lopes

Leads

Research Areas

Rui Lopes

Project

Genome Wide Association and Pharamcokinetics of Statin Therapies

We study genetic predictors of statin metabolism, efficacy, and tolerability in a post-cardiac event population.

Leads

Research Areas

Project

Learning Representations for Biomolecular Interaction Prediction

Deep neural networks are applied to predicting protein-ligand binding affinity by learning representations of atoms, atomic interactions, and inter-molecular interfaces.

Leads

Research Areas

Project

Blood Pressure Imager

Vision Group

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

Computational Connectomics Group

Robotics

Mumin Jin

Aya G. Halawi

Jianhua Li

Project

Understanding neural networks in the brain

We aim to develop fully automated algorithms for mapping networks within biological brains.

Nir Shavit

Leads

Nir Shavit

Research Areas

Clinical Decision-Making Group

Nir Shavit

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

Clinical Decision-Making Group

Project

Using Clinical Domain Knowledge for Processing Physiological Data

Using medical knowledge to improve learned representations of patient health trajectories.

Leads

Research Areas

Impact Areas

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

Covering All K-mers Using Joker Characters

9 People Results matching all criteria

Leandro Agudelo

Research Scientist

Systems Genomics, CompBio, Topological Integrative Biology, Biology

E lagudelo@csail.mit.edu

Systems Genomics, CompBio, Topological Integrative Biology, Biology

PI
Core/Dual

Bonnie Berger

Professor

Computational biology, genomics

E bab@csail.mit.edu

T 253-1827

Lead

Towards Context-Aware Functional Genomics

Metagenomic Binning Through Low-Density Hashing

Computation Structures Group

Member

Applied Machine Learning Community of Research

Computational biology, genomics

Barış Ekim

Graduate Student

E ekim@csail.mit.edu

PI
Core/Dual

John Guttag

Professor

Machine learning, medicine, data-driven inference

E guttag@csail.mit.edu

T 253-6022

Lead

Data-Driven Inference Group

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

Konstantin Krismer

Graduate Student

E krismer@mit.edu

Member

Computational Genomics Group

Alexander Lenail

Graduate Student

E lenail@csail.mit.edu

Samuel Sledzieski

Graduate Student

E samsl@csail.mit.edu

Joshua Tenenbaum

Professor

Computational cognitive science, machine learning

E jbt@csail.mit.edu

T 452-2010

RM 46-4015

Lead

Computational Cognitive Science Group

Member

Center for Brains, Minds and Machines

Single-Image 3D Interpreter Network

3D Generative Adversarial Networks

Embodied Intelligence Community of Research

Visual Computing Community of Research

Computational cognitive science, machine learning

Neil Thompson

Research Scientist

I'm an innovation scholar studying computer science and economics.

E neil_t@mit.edu

Lead

Supertech Research Group

Determining Wikipedia's Influence on Science

I'm an innovation scholar studying computer science and economics.

15 News Results matching all criteria

AI & ML Computational Biology Graphics & Vision Health Care

February 16 '21

Golland named 2021 AIMBE Fellow

Dr. Polina Golland to be inducted into medical and biological engineering elite

Golland named 2021 AIMBE Fellow

Algorithms & Theory AI & ML Computational Biology Health Care

January 27 '21

Robust AI tools to predict future cancer

Researchers created a risk-assessment algorithm that shows consistent performance across datasets from US, Europe and Asia.

Robust AI tools to predict future cancer

AI & ML Computational Biology Health Care

January 21 '21

Model analyzes how viruses escape the immune system

Using this computational system, researchers can identify viral protein sequences that could make better vaccine targets.

Model analyzes how viruses escape the immune system

Algorithms & Theory AI & ML Computational Biology Health Care

November 04 '20

Algorithm reduces unnecessary use of antibiotics for UTIs

Machine learning model predicts probability that a patient’s UTI can be treated by various antibiotics

Algorithm reduces unnecessary use of antibiotics for UTIs

Computational Biology

September 25 '19

Josh Tenenbaum receives 2019 MacArthur Fellowship

Brain and cognitive sciences professor studies how the human mind is able to learn so rapidly.

Josh Tenenbaum receives 2019 MacArthur Fellowship

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

Algorithms & Theory Computational Biology

May 08 '19

CSAIL's Daskalakis wins ACM Grace Murray Hopper Award

Constantinos (“Costis”) Daskalakis, an MIT professor and CSAIL principal investigator, has won the 2018 ACM Grace Murray Hopper Award.

CSAIL's Daskalakis wins ACM Grace Murray Hopper Award

AI & ML Computational Biology Health Care

May 07 '19

Using AI to predict breast cancer and personalize care

Image-based AI model can predict breast cancer years in advance

Using AI to predict breast cancer and personalize care

Computational Biology Health Care

May 06 '19

Merging cell datasets, panorama style

Algorithm stitches multiple datasets into a single “panorama,” which could provide new insights for medical and biological studies.

Merging cell datasets, panorama style

Algorithms & Theory Computational Biology Health Care

March 26 '19

Model learns how individual amino acids determine protein function

Technique could improve machine-learning tasks in protein design, drug testing, and other applications.

Model learns how individual amino acids determine protein function

AI & ML Computational Biology Security & Cryptography Cybersecurity Health Care

October 18 '18

Cryptographic protocol enables greater collaboration in drug discovery

Neural network that securely finds potential drugs could encourage large-scale pooling of sensitive data.

Cryptographic protocol enables greater collaboration in drug discovery

AI & ML Computational Biology Health Care

October 04 '18

Regina Barzilay joins leadership of new effort on machine learning in health

MacArthur “geniuses” in machine learning and synthetic biology to serve as faculty co-leads; Nobel laureate to chair advisory board of new research center.

Regina Barzilay joins leadership of new effort on machine learning in health

Computational Biology Health Care

May 22 '18

Gauging language proficiency through eye movement

Study tracks eye movement to determine how well people understand English as a foreign language.

Gauging language proficiency through eye movement

Computational Biology Security & Cryptography Cybersecurity Health Care

May 10 '18

Protecting confidentiality in genomic studies

Genome-wide association studies, which look for links between particular genetic variants and incidence of disease, are the basis of much modern biomedical research.

Protecting confidentiality in genomic studies

Machine learning approach could aid the design of industrial processes for drug manufacturing.

AI & ML Computational Biology Health Care

June 27 '17

Computer system predicts products of chemical reactions

When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it. There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.

Computer system predicts products of chemical reactions

13 Group Results

Community of Research

Applied Machine Learning Community of Research

Una-May O'Reilly

Leads

Una-May O'Reilly

Research Areas

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 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

Manufacturing

Lead

Peter Szolovits

+10

Peter Szolovits

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

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

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

Security & Cryptography

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

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

19 Project Results

Project

Artificial tissue homeostasis

Leads

Research Areas

Impact Areas

Project

Blood Pressure Imager

Vision Group

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

Robotics

Mumin Jin

Aya G. Halawi

Jianhua Li

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

Covering All K-mers Using Joker Characters

We developed a new algorithm to generate compact sequence sets covering all k-mers using joker characters.

Bonnie Berger

Leads

Bonnie Berger

Research Areas

Bonnie Berger

Project

Drug Target Identification Via Computational Modeling

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

Project

Fast & Accurate Alignment of Barcoded Short Reads

This project aims to develop an improved alignment algorithm for barcoded short reads as produced by 10X Genomics' sequencing platform.

Leads

Research Areas

Project

Flexible cellular platforms for reconfigurable biocircuits

Leads

Research Areas

Impact Areas

Project

Genome Wide Association and Pharamcokinetics of Statin Therapies

We study genetic predictors of statin metabolism, efficacy, and tolerability in a post-cardiac event population.

Leads

Research Areas

Project

Learning Representations for Biomolecular Interaction Prediction

Deep neural networks are applied to predicting protein-ligand binding affinity by learning representations of atoms, atomic interactions, and inter-molecular interfaces.

Leads

Research 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

Modeling of Targeted Liposomal Doxorubicin Dynamics

Rui Lopes

Leads

Research Areas

Rui Lopes

Project

Population-level Tumor Growth Inhibition Modeling

Leads

Research Areas

Impact Areas

Computational Biology Group

Project

Rational Design of Therapeutics

We use all-atom biophysical models to design inhibitors for hard-to-drug targets

Leads

Research Areas

Computational Connectomics Group

Project

Reconstructing Neural Circuits from Mammalian Brain

Yaron Meirovitch

Nir Shavit

Leads

Yaron Meirovitch

Nir Shavit

Research Areas

Impact Areas

Clinical Decision-Making Group

Yaron Meirovitch

Nir Shavit

Project

Semi-Supervised Regression with Cycle Wasserstein Regression GANs