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.
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.
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.
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. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. 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
ACL Fellowship 2017
AAAI Fellowship 2018
Top 100 AI Leaders in Drug Discovery & Advanced Healthcare 2019