ECE Seminar Series

Department of Electrical and Computer Engineering, and Department of Computer Science Special Seminar Sponsored by ISTeC

Title: Distributed Inference: Aggregating Probability Forecasts and Collaborative Regression
Speaker: Sanjeev Kulkarni
Affiliation: Princeton University
Day: Monday, October 24, 2016
Time: 3:00 pm - 4:00 pm
Location: Lory Student Center 300

Abstract: A recent area of interest in machine learning involves drawing inferences from a large number of agents, each with some partial information. Often, these tasks must be accomplished in a distributed setting and in the face of scarce resources (time, bandwidth, and power). This talk describes some of our work on two problems in this area that use ideas from kernel methods and graphical models: (i) aggregating probability forecasts; and (ii) an approach for collaborative regression.

Bio: Sanjeev R. Kulkarni received a B.S. in Mathematics and Electrical Engineering from Clarkson University, an M.S. in Electrical Engineering from Stanford University, and his Ph.D. in Electrical Engineering from M.I.T. From 1985 to 1991 he was a Member of the Technical Staff at M.I.T. Lincoln Laboratory. Since 1991, he has been with Princeton University where he is currently Professor of Electrical Engineering and Dean of the Graduate School. He is also an affiliated faculty member of the Department of Operations Research and Financial Engineering and the Department of Philosophy. Prof. Kulkarni served as Director of the Keller Center from 2011-2014, Master of Butler College from 2004 to 2012, and Associate Dean for Academic Affairs in the School of Engineering and Applied Science from 2003-2005. He spent January 1996 as a research fellow at the Australian National University. He spent 1998 with Susquehanna International Group and was a regular consultant there from 1997 to 2001. During Summer 2001, he was a visiting researcher at Flarion Technologies. Prof. Kulkarni received an ARO Young Investigator Award in 1992, and an NSF Young Investigator Award in 1994. He has also received a number of teaching awards at Princeton University, including the President's Award for Distinguished Teaching in May 2007. He served as an Associate Editor for the IEEE Transactions on Information Theory and is a Fellow of the IEEE. Prof. Kulkarni's research interests include statistical pattern recognition, machine learning, nonparametric estimation, information theory, wireless networks, signal/image/video processing, and econometrics and finance.