Events details

Artificial Intelligence for Oncology: Learning to Cure Cancer from Images and Text

A talk by Professor Regina Barzilay, MIT CSAIL

Winner of 2017 MacArthur ‘genius grant’

At Education City Student Center

4pm, Tuesday, March 27

Abstract:

The majority of cancer research today takes place in biology and medicine. Artificial intelligence plays a minor supporting role in this progress if at all. In this talk, I will focus on machine learning models that are already making a difference in clinical practice. Examples of these methods include automatic reading of imaging data, large-scale analytics over patient records, and improved models of disease progression.  I will show tools that are already implemented at Massachusetts General Hospital and are used to inform patient care. This part of the talk draws on active collaboration with physicians at MGH Cancer Center.

In the second part of the talk, I will push beyond standard tools, introducing new functionalities and avoiding annotation-hungry training paradigms ill-suited for clinical practice. Specifically, I will focus on interpretable neural models that provide rationales underlying their predictions, and transfer models that robustly handle data in different domains.

At the beginning of the talk, I will include a short tutorial on machine learning that will provide necessary background material.

Bio:

Regina Barzilay is a Delta Electronics professor in EECS and a member of CSAIL. Her research interests are in machine learning, 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 andseveral Best Paper Awards in top conferences in her field. In 2017, she received a MacArthur fellowship.  She is a fellow of American Association of Artificial Intelligence (AAAI) and a fellow of Association of Computational Linguistics (ACL). She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.


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