Professor, Department of Electrical Engineering and Computer Science; Faculty Co-Lead, MIT Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic)
Regina Barzilay is a Delta Electronics Professor in MIT’s Department of Electrical Engineering and Computer Science and a faculty lead for the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic). She is also a principal investigator at the Computer Science and Artificial Intelligence Laboratory. Barzilay’s research interests are in natural language processing, and applications of deep learning to chemistry and oncology. Her research focuses on 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. Barzilay has received an NSF Career Award, MIT Technology Review’s TR-35 Award, and Microsoft Faculty Fellowship. She has also received fellowships from the MacArthur Foundation, Association for Computational Linguistics and Association for the Advancement of Artificial Intelligence. She received a PhD in computer science from Columbia University, and was a postdoc at Cornell University.
- Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., Palmer, A., Settels, V., Jaakkola, T., Jensen, K., Barzilay, R. (2019). Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling, 59 (8), pp. 3370-3388.
- Luo, J., Cao, Y., Barzilay, R. (2019). Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 3146–3155.
- Yala, A., Lehman, C., Schuster, T., Portnoi, T., Barzilay, R. (2019). A Deep Learning Mammography-Based Model for Improved Breast Cancer Risk Prediction. Radiology 92:1, pp. 60-66.
- Schuster, T., Ram, O., Barzilay, R., Globerson, A. (2019). Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HST), Volume 1, pp. 1599–1613.
- Sept. 23, 2020: MIT News, Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award.
- Feb. 20, 2020: MIT News, Artificial intelligence yields new antibiotic.
- Nov. 16, 2019: New York Times Magazine, From gene editing to A.I., how will technology transform humanity?
- July 1, 2019: MIT Tech Review, Machine learning has been used to automatically translate long-lost languages.
- Feb. 21, 2019: New York Times, Looking to technology to avoid doctor’s offices and emergency rooms.