Bahman did his PhD at Stanford University, supported by William R. Hewlett Stanford Graduate Fellowship, and focused on the topic of algorithms for big data applications, in which he is a well-published author in some of the best conferences and journals, including PVLDB, SIGMOD, WWW, and KDD. He was the last PhD student of the legendary late Rajeev Motwani, and has been also advised and co-advised by Ashish Goel and Prabhakar Raghavan (formerly Yahoo VP of Strategy, currently Google VP of Engineering). His industry experience during his PhD studies spans several internships and collaborations with some of the best researchers and practitioners from Twitter, Microsoft Research, Yahoo Research, AOL, and Google. He is a recipient of the Yahoo Key Scientific Challenges Award for his contributions to the area of search technologies.
5:05pm–5:45pm Thursday, 10/16/2014
Location: Table E
If security is a priority for you, stop by to visit with Bahman. (And if it isn’t a priority, you should probably attend his Strata session). He’ll talk about machine learning algorithms for detecting adversaries, attacks against ML algorithms, and making ML algorithms robust against adversaries.
2:35pm–3:15pm Friday, 10/17/2014
Location: 1 E10/1 E11
As in a game of chess, successful use of machine learning techniques against adaptive adversaries, such as spammers and intruders, requires designing the learning algorithms having anticipated the opponent’s response to those algorithms. In this talk, we present techniques to design robust machine learning algorithms for adversarial environments and provide clarifying attack-defense examples.