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SUMMARY:ECE Seminar: Data efficient high-dimensional machine learning
DESCRIPTION:Speaker: Dr. Kamyar Azizzadenesheli, Assistant Professor, Department of Computer Science, Purdue University
Talk Title: Data efficient high-dimensional machine learning
Abstract: Traditional deep neural networks are maps between finite dimension spaces, and hence, are not suitable for modeling phenomena such as those arising from the solution of partial differential equations (PDE). In the first part of the talk, I introduce a new deep learning paradigm, called neural operators, that learns operators which are maps between infinite dimension spaces. I show that neural operators are universal approximators of operators and demonstrate a series of empirical successes of neural operators in natural sciences. \n
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In the second part, I talk about the intersection of control theory and reinforcement learning and establish data-efficient learning and decision-making methods for generic dynamical systems. I conclude the talk by presenting empirical successes of these principled methods.
Biography: Kamyar Azizzadenesheli is an assistant professor at Purdue University, department of computer science, since Fall 2020. Prior to his faculty position, he was at the California Institute of Technology (Caltech) as a Postdoctoral Scholar at the Department of Computing + Mathematical Sciences. Before his postdoctoral position, he was appointed as a special student researcher at Caltech, working with ML and Control researchers at the CMS department and the Center for Autonomous Systems and Technologies. He is also a former visiting student researcher at Caltech. Kamyar Azizzadenesheli is a former visiting student researcher at Stanford University, and researcher at Simons Institute, UC. Berkeley. In addition, he is a former guest researcher at INRIA France (SequeL team), as well as a visitor at Microsoft Research Lab, New England, and New York. He received his Ph.D. at the University of California, Irvine.
Host: Dr. Salman Avestimehr, avestime@usc.edu
Webcast: https://usc.zoom.us/j/93153496285?pwd=SmE3clJMSm9OVmVoNWdhMW1SVlk4QT09
DTSTART:20220316T100000
LOCATION:EEB 248
URL;VALUE=URI:
DTEND:20220316T110000
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