Social Learning and Networking: An Engineering View
13 Apr 2012
Date : 13 Apr 2012
Time : 2:30 pm - 3:30 pm
Venue : Room PQ703, 7/Floor, PQ core, Mong Man Wai Building
The Hong Kong Polytechnic University
With the ubiquitous popularity of social networks, people nowadays are immersed with avalanche of information to interact with ever increasing number of people. This trend poses an urgent need to study the impact of human behavior on social networks by analyzing how users learn from their own and/or shared observations and make intelligent decisions. Indeed, most modern engineering problems do have similar characteristic, however, the influence of behavior dynamics and interactions among cognitive users has seldom been recognized and taken advantage of.
In the first part of my talk, I will illustrate why and how understanding interactions and dynamics can ultimately offer engineering systems with better performance, and demonstrate with examples that the social networking viewpoint can offer a new idea and thinking to revolutionize the formulation of many traditional and emerging engineering problems. Then in the second part of my talk, I will point out the issue that traditional engineering problems often assume that users are always cooperative no matter what, which is taken for granted. By borrowing the concept of indirect reciprocity from biology, I will show how to stimulate cooperation in engineering problems to achieve better system performance.
Finally, in the last part of my talk, I will discuss the general social learning and networking problems where users have uncertainty about the system state and make decisions sequentially. In such a case, users who make decisions later can learn from previous users and their decisions influence each other. Such social learning and networking problems involve both the decision making and learning. There is no existing tool that can be directly used to solve these problems. By introducing the strategic decision making into the well-known Chinese restaurant process, we develop a new fundamental tool, called Chinese restaurant game, to study these social learning and networking problems. Indeed, we are building a bridge between machine learning and strategic decision making. We demonstrate with applications that such a fundamental tool is very general and can be applied into many different fields.
Yan Chen is a postdoctoral research associate in the Department of Electrical and Computer Engineering at University of Maryland College Park. He received the Bachelor's degree from University of Science and Technology of China (USTC) in 2004, the M.Phil degree from Hong Kong University of Science and Technology (HKUST) in 2007, and the Ph.D. degree from University of Maryland College Park in 2011. His current research interests are in social learning and networking, smart grid, cloud computing, crowdsourcing, network economics, and multimedia signal processing. He received the University of Maryland Future Faculty Fellowship in 2010, Chinese Government Award for outstanding students abroad in 2011, University of Maryland ECE Distinguished Dissertation Fellowship Honorable Mention in 2011, and was the Finalist of A. James Clark School of Engineering Dean's Doctoral Research Award in 2011. He has published more than 40 scholarly publications.
*** ALL ARE WELCOME ***
Contact: Prof. George Baciu
Tel : 2766-7295 or 2766-7272