Bandits and Newsvendors: Joint Online Learning and Optimization in Wireless Networks

Wednesday, November 04, 2015
12:00 PM to 1:00 PM
WAG 101
Free and open to the public

Algorithms for online learning and decision-making under uncertainty have become popular in recent years to improve the performance of wireless networks in unknown dynamic environments. I will give a brief overview of certain classic problem formulations such as multi-armed bandits (MAB) and newsvendor problems, talk about their applications to wireless networking, and present some recent results from my group's research in this area. These include results for decentralized MAB, combinatorial MAB, contextual MAB, multi-period newsvendors, and optimized robotic network formation in unknown environments.

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Prof. Bhaskar Krishnamachari

Prof. Bhaskar Krishnamachari

Associate Professor
University of Southern California

Bhaskar Krishnamachari is an Associate Professor and Ming Hsieh Faculty Fellow in Electrical Engineering, and Director of the Autonomous Networks Research Group at the University of Southern California's Viterbi School of Engineering.  He received his undergraduate degree from The Cooper Union in New York (1998) and his MS (1999) and Ph.D. (2002) from Cornell University, all in Electrical Engineering. He works on the design and analysis of algorithms and protocols for next generation wireless networks. His co-authored papers have received best paper awards at IPSN (2004, 2010), MSWiM (2006) and MobiCom (2010), a best paper runner-up at SECON (2012), and a top-three paper at MSWiM (2014). He has received the NSF CAREER award (2004), the ASEE Terman Award (2010), and has been included on Technology Review Magazine's TR-35 list (2011), and Popular Science's Brilliant 10 list (2015). He has authored a book titled "Networking Wireless Sensors" published by Cambridge University Press. He is an Editor for the ACM Transactions on Sensor Networks, and TPC Co-Chair for WiOpt 2016.