MACHINE LEARNING & AI FOR HEALTHCARE: A HIMSS EVENT
Boston, MA, June 13-14, 2019
Regina Barzilay is a 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. Currently, Prof. Barzilay is focused on bringing the power of machine learning to oncology. In collaboration with physicians and her students, she is devising deep learning models that utilize imaging, free text, and structured data to identify trends that affect early diagnosis, treatment, and disease prevention. Prof. Barzilay is poised to play a leading role in creating new models that advance the capacity of computers to harness the power of human language data.
Prof. Barzilay is a recipient of various awards including an NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards in top NLP conferences. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship.
Prof. Barzilay received her MS and BS from Ben-Gurion University of the Negev. Regina Barzilay received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.
Healthcare is still in a primitive stage of using data science and machine learning to detect and treat cancer. In this session, MIT Professor Regina Barzilay explains how the school’s Computer Science and Artificial Intelligence Laboratory is training deep learning models to outperform human oncologists.
Cancer affects millions of people, and Professor Barzilay sees machine learning playing a transformative role in improving access to care, reducing costs, and decreasing mortality. She’ll give attendees a fascinating and inspiring look how she and her team, collaborating with Massachusetts General Hospital, are developing algorithms and using NLP to improve models for disease progression, prevent overtreatment, and personalize the cure.