ACM SIGMETRICS 2017

Champaign-Urbana, USA
5-9 June, 2017

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 Keynote


Speaker: Michael Jordan
Professor, University of California Berkely

Title: TBD

Abstract: TBD

Bio: Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.

 Keynote


Speaker: Vahab Mirrokni
Principle Scientist, Google Research, New York

Title: TBD

Abstract: TBD

Bio: Vahab Mirrokni is a principal scientist, heading the algorithms research groups at Google Research, New York. He received his PhD from MIT in 2005 and his B.Sc. from Sharif University of Technology in 1999. He joined Google Research in 2008, after spending a couple of years at Microsoft Research, MIT and Amazon.com. He is the co-winner of paper awards at KDD'15, ACM EC'08, and SODA'05. His research areas include algorithms, distributed and stochastic optimization, and computational economics. At Google, he is mainly working on algorithmic and economic problems related to search and online advertising. Recently he is working on online ad allocation problems, distributed algorithms for large-scale graph mining, and mechanism design for advertising exchanges.