Regina Barzilay

Delta Electronics Professor, EECS

MacArthur Fellow

MIT Computer Science & Artificial Intelligence Lab

32 Vassar Street, 32-G468

Cambridge, MA 02139

(617) 258-5706


Regina Barzilay wins MacArthur “genius grant”

MIT computer scientist who studies natural language processing and machine learning wins MacArthur grant.

MIT Professor Regina Barzilay on use of computer data in cancer treatment

Regina Barzilay discusses her own experience with the disease and how she uses data and machine learning to advance detection and treatment.

Computer system predicts products of chemical reactions

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

Research Interests


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.


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

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 pipeline. In a joint work with chemical engineers and chemists at MIT, we are working on deep learning methods for modeling chemical processes.


Style Transfer from Non-Parallel Text by Cross-Alignment

Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola

Proceedings of NIPS, 2017

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

Wengong Jin, Connor W. Coley, Regina Barzilay and Tommi Jaakkola

Proceedings of NIPS, 2017

Unsupervised Learning of Morphological Forests

Jiaming Luo, Karthik Narasimhan and Regina Barzilay

Transactions of the Association for Computational Linguistics (TACL), 2017.

Aspect-augmented Adversarial Networks for Domain Adaptation

Yuan Zhang, Regina Barzilay and Tommi Jaakkola

Transactions of the Association for Computational Linguistics (TACL), 2017.

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Regina Barzilay is a Delta Electronics professor 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. Her research interests are in natural language processing, 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. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.


Best Paper Award, HLT/NAACL 2004

Technology Research News: “Top Picks: Technology Research Advances of 2004”

NSF Career Award 2005

Technology Review: 35 Top Innovators 2005

IEEE Intelligent Systems: “AI Ten to Watch” 2006

Microsoft Faculty Fellowship 2006

Ross Career Development Professor 2006

Best Paper Award, ACL 2009

Carolyn Baldwin Morrison lecture, Cornell 2009

Best Paper Award, SLT 2010

Best Student Paper Award, NAACL 2014

Faculty Research Innovation Fellowship 2014

Best Paper Honorable Mention, EMNLP 2015

Delta Electronics Professor 2015

Burgess & Elizabeth Jamieson Award for Excellence in Teaching 2016

Best Paper Award, EMNLP 2016

MacArthur Fellowship 2017