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. He has published 30 papers on Bayesian inference in open universe probabilistic models, probabilistic programming language, large-scale learning, time series, and social networks, and holds three US patents. He has served in the Program Committee for ICML 2014, ECML/PKDD 2014, SDM 2013/2014, IJCAI 2011/2013, and as a lecturer in 2014 summer school on Probabilistic Programming for Advancing Machine Learning. 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. He worked briefly at Microsoft Research (Asia and Redmond), Google (Mountain View), and IBM (TJ Watson Reserch Center).

Lei received his B.S. in Computer Science and Engineering from Shanghai Jiao Tong University in 2006 (ACM honored class) 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).