Jun 3rd, 2016, 11am-12pm, DBH 6011
Bandits and Newsvendors: Joint Online Learning and Optimization in Wireless Networks
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. This talk will cover joint work with students Dr. Yi Gai, Dr. Yanting Wu, Pranav Sarkar, Parisa Mansourifard, and Shangxing Wang, and faculty collaborators Rahul Jain, Tara Javidi, and Nora Ayanian.
Bhaskar Krishnamachari is a Professor and Ming Hsieh Faculty Fellow in Electrical Engineering, and Director of the Autonomous Networks Research Group (http://anrg.usc.edu/) 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). 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.