Xin Liu
Professor, University of California, Davis
March 2nd, 2018, 11am-12pm, DBH 6011
Title:
Data-driven Approach in Networking
Abstract:
Based on network measurement and user behavior data, much recent work
has studied the modeling and prediction of network utility and user
experience using machine learning techniques. While it provides
important insights, such modeling and prediction is often not the
ultimate goal of networks. Ideally, a network should be able to learn
its optimal control and resource allocation that maximize network
utility and user experience in a proactive manner. To achieve this
goal, machine learning and network resource optimization techniques
can play a significant role. In this talk, we discuss some of the
challenges and opportunities that we observe in data-driven network
control and resource allocation. Using examples from real network
control challenges, we discuss both applied and theoretical aspects of
data-driven networking.
Speaker Bio:
Xin Liu received her Ph.D. degree in electrical engineering from
Purdue University in 2002. She is currently a Professor in the
Computer Science Department at the University of California, Davis.
>From March 2012-June 2014, she worked in the wireless networking
group at Microsoft Research Asia. She has studied cellular scheduling
algorithms, cognitive radio networks, and wireless mesh networks. Her
current research focuses on data-driven approaches in networking. She
has received the NSF CAREER award (2005), and the Outstanding
Engineering Junior Faculty Award from the UC Davis College of
Engineering (2005), and the Chancellor's Fellowship (2011), and the
ICNP best paper award (2017).