Regina Barzilay

School of Engineering Distinguished Professor for AI and Health

AI Faculty Lead, Jameel Clinic

MacArthur Fellow

MIT Computer Science & Artificial Intelligence Lab

32 Vassar Street, 32-G468

Cambridge, MA 02139

(617) 258-5706

regina@csail.mit.edu

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Perturbation, Therapeutics and Machine Learning Workshop (jointly organized by Jameel Clinic and Eric and Wendy Schmidt Center)

September 2021

Announcements

News

These Doctors Are Using AI to Screen for Breast Cancer

During the pandemic, thousands of women have skipped scans and check-ups. So physicians tapped an algorithm to predict those at the highest risk.

A leading AI researcher calls for standards to ensure equity and fairness

A top researcher at the Massachusetts Institute of Technology on Thursday said that artificial intelligence systems developed for medicine must be more transparent and judged against a set...

MIT Professor Who Advanced Cancer Treatment Wins $1 Million AI Prize

Regina Barzilay, a breast cancer survivor, was recognized for her work in breast-cancer detection and drug discovery.

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Machine learning has been used to automatically translate long-lost languages

Some languages that have never been deciphered could be the next ones to get the machine translation treatment.

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Can these researchers catch cancer much earlier than ever before?

From revolutionizing the mammogram to spotting a single tumor cell in the blood...

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Looking to Technology to Avoid Doctors’ Offices and Emergency Rooms

Americans are eagerly turning to the latest tech devices in hopes of preventing and detecting medical problems early...

News

Research Interests

NLP

Natural Language Processing

I develop machine learning models that aim to understand and generate natural languages. We are currently witnessing the first generation of NLP tools that have been deployed at scale and are used by millions of people. However, the major component of this success is access to large amounts of training data which machines use to learn mappings between input and output. In many applications and languages, such annotations are not readily available, and are expensive and slow to collect. I am interested in designing algorithms that do not suffer from this annotation dependence. Specifically, we are developing deep learning models that can transfer annotations across domains and languages, that can learn from a few annotated examples by utilizing supplementary data sources, and that can take advantage of human-provided rationales to constrain model structure.

Oncology

Learning to Cure

Data collected about millions of cancer patients — their pathology slides, imaging, and other tests — contain answers to many open questions in oncology. Jointly with the MGH collaborators, we are developing algorithms that can learn from this data to improve models of disease progression, prevent over-treatment, and narrow down to the cure. On the NLP side, we are creating databases which record pertinent cancer features extracted from raw documents. On the computer vision side, we are working on deep learning models that compute personalized assessment from mammogram data focusing on early cancer detection.

Chemistry

ML Drug Discovery

Today, drug discovery involves practitioners with years of advanced training and is carried out in a trial-and-error, labor-intensive fashion. Our goal is to change a traditional discovery pipeline. In a joint work with chemical engineers and biologists at MIT, we are working on deep learning methods for modeling biological and physicochemical properties, de-novo molecular design, and retrosynthesis. On the ML side, this area brings many interesting questions related to learning molecular representations, interpretability and robustness. As part of the MLPDS consortium, we are continuously learning from the deployment of our models in the pharmaceutical industry, directing the development towards our ultimate goal to change the drug discovery process.

Research Interests

Papers

Deciphering Undersegmented Ancient Scripts Using Phonetic Prior

Luo, Jiaming, Frederik Hartmann, Enrico Santus, Yuan Cao, and Regina Barzilay

TACL, 2021.

A Deep Learning Approach to Antibiotic Discovery

Stokes, Jonathan M., Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackermann,..., Tommi S. Jaakkola, Regina Barzilay, James J. Collins

Cell, 2020.

Multi-Objective Molecule Generation using Interpretable Substructures

Jin, Wengong, Regina Barzilay, and Tommi Jaakkola

Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.

View all

Papers

Bio / Awards

Group

Teaching

Yujia Bao

Benson Chen

Charles Comiter

Gabriele Corso

Felix Faltings

Adam Fisch

Octavian Ganea

Jiang Guo

Bracha Laufer

Peter Mikhael

Jason Yim

UROP Applications Open for the Spring Semester 2021

Apply here

Spring 2021 6.402/6.482 Modeling with Machine Learning: From Algorithms to Applications

Bio

Awards

Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. She is an AI faculty lead for Jameel Clinic, an MIT center for Machine Learning in Health. Her research interests are in natural language processing and applications of deep learning to chemistry and oncology. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. In 2020, she was awarded the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. She received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University. Prof. Barzilay received her undergraduate degree from Ben-Gurion University of the Negev, Israel.

AACC Wallace H. Coulter Lectureship Award 2021
UNESCO/Netexplo Award 2021
AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity 2021
Top 100 AI Leaders in Drug Discovery & Advanced Healthcare 2019
Xconomy Boston Digital Trailblazer 2019
Susan Komen Scholar 2018
Ruth and Joel Spira Award for Excellence in Teaching 2018
AAAI Fellowship 2017
ACL Fellowship 2017
MacArthur Fellowship 2017
Best Paper Award, EMNLP 2016
Burgess & Elizabeth Jamieson Award for Excellence in Teaching 2016
Delta Electronics Professor 2016
Best Paper Honorable Mention, EMNLP 2015
Faculty Research Innovation Fellowship 2014
Best Student Paper Award, NAACL 2014
Best Paper Award, SLT 2010
Carolyn Baldwin Morrison Lecture, Cornell 2009
Best Paper Award, ACL 2009
Ross Career Development Professor 2006
Microsoft Faculty Fellowship 2006
IEEE Intelligent Systems: “AI Ten to Watch” 2006
Technology Review: 35 Top Innovators 2005
NSF Career Award 2005
Technology Research News: “Top Picks: Technology Research Advances of 2004”
Best Paper Award, HLT/NAACL 2004

Teaching / Students