Sep 23–26, 2019
Rich Caruana

Rich Caruana
Principal Researcher, Microsoft

Rich Caruana is a principal researcher at Microsoft Research. Previously, he was on the faculty in the Computer Science Department at Cornell University, at UCLA’s Medical School, and at Carnegie Mellon University’s Center for Learning and Discovery. Rich received an NSF CAREER Award in 2004 (for meta clustering); best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles); co-chaired KDD in 2007 (with Xindong Wu); and serves as area chair for Neural Information Processing Systems (NIPS), International Conference on Machine Learning (ICML), and KDD. His research focus is on learning for medical decision making, transparent modeling, deep learning, and computational ecology. He holds a PhD from Carnegie Mellon University, where he worked with Tom Mitchell and Herb Simon. His thesis on multi-task learning helped create interest in a new subfield of machine learning called transfer learning.


11:20am12:00pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Ethics
Harsha Nori (Microsoft), Samuel Jenkins (Microsoft), Rich Caruana (Microsoft)
Understanding decisions made by machine learning systems is critical for sensitive uses, ensuring fairness, and debugging production models. Interpretability presents options for trying to understand model decisions. Harsha Nori, Sameul Jenkins, and Rich Caruana explore the tools Microsoft is releasing to help you train powerful, interpretable models and interpret existing black box systems. Read more.
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