Speaker: Salman Avestimehr (USC)


Title: Coded Computing




This talk introduces "Coded Computing”, a new framework that brings concepts and tools from information theory and coding theory into distributed computing to mitigate several performance bottlenecks that arise in large-scale machine learning. It is demonstrated how coded computing achieves the optimal tradeoff between “communication load” and “computation load” in distributed computing, and how it provides an optimal toleration for straggling nodes by injecting computation redundancy in unorthodox coded forms. The practical impact of coded computing is also demonstrated in several applications. The talk will conclude by discussing several open problems in this area.




Salman Avestimehr is an Associate Professor at the Electrical Engineering Department of University of Southern California. He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science, both from the University of California, Berkeley. Prior to that, he obtained his B.S. in Electrical Engineering from Sharif University of Technology in 2003.  His research interests include information theory, distributed computing, and data analytics. Dr. Avestimehr has received a number of awards, including a Communications Society and Information Theory Society Joint Paper Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), a Young Investigator Program (YIP) award from the U. S. Air Force Office of Scientific Research, a National Science Foundation CAREER award, and several best paper awards. He is currently an Associate Editor for the IEEE Transactions on Information Theory and a General Co-Chair of the 2020 International Symposium on Information Theory (ISIT).