Michael I. Jordan — Panel
|Event Date:||April 11, 2019|
|Speaker:||Michael I. Jordan|
|Speaker Affiliation:||Pehong Chen Distinguished Professor
Dept. of Electrical Engineering & Computer Science and Dept. of Statistics
University of California, Berkeley
|Sponsor:||Purdue Electrical & Computer Engineering|
|Location:||Herman & Heddy Kurz Atrium, ARMS|
|Contact Name:||Marsha Freeland
|Contact Phone:||+1 765 49-45341
|School or Program:||College of Engineering
Statistical vs Empirical Learning: Which will prevail?
Statistical learning is a well-established subject. It offers data modeling, inference methodologies, quantitative assessment, and tools we normally find in today’s data-related applications. Meanwhile, in the past 10 years we have witnessed the success of empirical learning approaches, namely the family of deep neural networks. These methods are not easy to explain, assess, and predict. Yet, they perform well. So, as we move forward to the next generation of machine learning, what will be the roles of statistical learning and empirical learning? Will statistical learning be able to explain empirical learning? Will empirical learning prevail statistical learning? Is it possible to bridge the gap? What are the key building blocks we are missing today? Please join us for the panel discussion with Professor Michael Jordan.
• Stanley Chan, Assistant Professor of Electrical and Computer Engineering and Statistics
• Charles Bouman, Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering
• David Gleich, Jyoti and Aditya Mathur Associate Professor of Computer Science
• Michael Jordan, Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley
• Juan Wachs, James A. and Sharon M. Tompkins Rising Star Associate Professor of Industrial Engineering
• Patrick Wolfe, Frederick L. Hovde Dean of Science and Miller Family Professor of Statistics and Computer Science
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences. Professor Jordan is a member of the National Academy of Sciences and a member of the National Academy of Engineering. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009.