Speaker: Prof. Gautam Kamath, University of Waterloo (and Faculty Member at the Vector Institute)
Moderator: Dr. Vishnu Pendala, San Jose State University

Meeting Date: (day), (date/month), 2021
Time: Checkin via Zoom for presentation at 6:00 (PDT)
Cost: none
Reservations: events.vtools.ieee.org/m/431191
Summary: After a brief introduction highlighting issues and solutions for data privacy in machine learning settings, Prof. Gautam Kamath will answer your questions related to data privacy, machine unlearning, research, academia, career advice, and anything in between. You may review his work and thoughts on Google Scholar at scholar.google.com/citations?user=MK6zHkYAAAAJ&view_op=list_works&sortby=pubdate , and on YouTube at www.youtube.com/results?search_query=gautam+kamath and generally on the Internet.
Please submit your questions in advance:
— via Twitter by using the hashtag, #KamathAMA and tagging @vishnupendyala
— emailing vspendyala(at)hotmail(dot)com with #KamathAMA in the subject
— during your registration on Zoom
Selected questions will be answered by Prof. Kamath during the session. Audience may be able to ask follow-up questions during the session using the Zoom Chat system.

Bio: Gautam Kamath is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo, and a Canada CIFAR AI Chair and Faculty Member at the Vector Institute. He has a B.S. in Computer Science and Electrical and Computer Engineering from Cornell University and an M.S. and Ph.D. in Computer Science from the Massachusetts Institute of Technology. He is interested in reliable and trustworthy statistics and machine learning, including considerations such as data privacy and robustness. He was a Microsoft Research Fellow, as a part of the Simons-Berkeley Research Fellowship Program at the Simons Institute for the Theory of Computing. He serves as an Editor in Chief of Transactions on Machine Learning Research and is the program committee co-chair of the 36th International Conference on Algorithmic Learning Theory (ALT 2025). He is the recipient of several awards, including the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies, a best paper award at the Forty-first International Conference on Machine Learning (ICML 2024), and the Faculty of Math Golden Jubilee Research Excellence Award.