Short Bio:

Dr. Lei Li is a Post-Doctoral researcher at EECS department of UC Berkeley. His research interest lies in the intersection of machine learning, statistical inference and database systems. Specifically, he has been working on Bayesian inference in open universe probabilistic models, probabilistic programming language, large-scale learning, time series, communication and social networks. He has served in the Program Committee for ICML 2014, SDM 2013/2014, IJCAI 2011/2013. He has been invited as reviewer for TOMCCAP, DAMI, TKDE, TOSN, Neurocomputing, KDD, SIGMOD, VLDB, PKDD and WWW. He has been invited to review NSF proposal in 2010 and to DARPA's Information Science and Technology (ISAT) probabilistic programming workshop in 2013.

Lei received his B.S. in Computer Science and Engineering from Shanghai Jiao Tong University in 2006 and Ph.D. in Computer Science from Carnegie Mellon University in 2011, respectively. His dissertation work on fast algorithms for mining co-evolving time series was awarded ACM KDD best dissertation (runner up).