Title: Coupon Allocation in Social Market : Robust and Machine Learning
Speaker: Prof. Ding-Zhu Du, University of Texas at Dallas, USA
Date/Time: October 24, 2021 / 9:00 - 10:00 UTC+8
Abstract: In this talk, we consider the coupon allocation problem in marketing. It has been reported that 40% of consumers will share an email offer with their friend and 28% of consumers will share deals via social media platforms. What does this mean for a business? Essentially discounts should not just be treated as short term solutions to attract individual customer, instead, allocating coupon to a small fraction of users (called seed users) may trigger a large cascade in a social market. This motivates us to study the influence maximization coupon allocation problem: given a social network and budget, we need to decide to which initial set users should offer the coupon, and how much should the coupon be worth. Our goal is to maximize the number of customers who finally adopt the target product. The talk is based on recent research paper of Jianxiong Guo et al..
Bio: Ding-Zhu Du received his M.S. degree in 1982 from Institute of Applied Mathematics, Chinese Academy of Sciences, and his Ph.D. degree in 1985 from the University of California at Santa Barbara. He worked as a postdoctor at Mathematical Sciences Research Institute, Berkeley in 1985-86, as an assistant professor at MIT in 1986-87, and as a research associate at Princeton University in 1990-91. He was an associate-professor/professor at Department of Computer Science and Engineering, University of Minnesota in 1991-2005, a Program Director for Theory of Computing at National Science Foundation of USA in 2002-2005, and a research professor at Institute of Applied Mathematics, Chinese Academy of Sciences, in 1987-2002. Currently, he is a professor at Department of Computer Science, University of Texas at Dallas. His research interest is in theory of computation, especially in design and analysis of approximation algorithms for combinatorial optimization problems with applications in computer and communication networks, and social networks. He has published more than 230 journal papers and more than 10 books. He is the founder of Journal of Combinatorial Optimization and an co-Editor-in-Chief of Computational Social Network and Discrete Mathematics, Algorithms and Applications. He is also in editorial boards of more than 15 journals.
Title: Learning Graphs with Topology Properties
Speaker: Prof. Tony Q.S. Quek, Singapore University of Technology and Design, Singapore
Date/Time: October 26, 2021 / 9:00 - 10:00 UTC+8
Abstract: Graphs are mathematical tools, consisting of nodes (vertices) and links (edges), used in various fields to represent, process, visualize, and analyze structured data. In many cases, datasets consist of an unstructured list of samples, and the underlying graph topology (representing the structural relations between samples) is unknown. It is thus desirable to learn the graph from data. Typically, graph learning is an ill-posed problem since multiple solutions may exist associating a graph with the data. In this talk, we show how constraints can be imposed directly on the learned graphs so as to enforce certain topology properties that can best fit the data. Specifically, inspired by a specific application domain (e.g., community detection), we develop a graph learning method that learns a graph with overlapping community structure. Our method encompasses and leverages the community structure information, along with attributes such as sparsity and signal smoothness to capture the intrinsic relationships between data entities, such that the estimated graph can optimally fit the data. Furthermore, we extend to more complex datasets with heterogeneous graph signals. In summary, our methods can incorporate topology properties in graph learning, which makes it possible to capture complex and non-typical behavior of graph signals that cannot be explicitly handled just by observed data.
Tony Q.S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, Tokyo, Japan, respectively. At Massachusetts
Institute of Technology (MIT), Cambridge, MA, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Cheng Tsang Man Chair Professor with
Singapore University of Technology and Design (SUTD) and the Visiting Chair Professor with CSIE at NCKU. He also serves as the Head of ISTD Pillar, Sector Lead for SUTD AI
Program, and the Deputy Director of SUTD-ZJU IDEA. His current research topics include wireless communications and networking, big data processing, network intelligence,
URLLC, and IoT.
Dr. Quek has been actively involved in organizing and chairing sessions and has served as a TPC member in numerous international conferences. He is currently serving as an Editor for the IEEE Transactions on Wireless Communications and an elected member of the IEEE Signal Processing Society SPCOM Technical Committee. He was an Executive Editorial Committee Member of the IEEE Transactions on Wireless Communications, an Editor of the IEEE Transactions on Communications, and an Editor of the IEEE Wireless Communications Letters. He is a co-author of a few books published by Cambridge University Press.
Dr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, the 2020 Nokia Visiting Professorship, and the 2016-2020 Clarivate Analytics Highly Cited Researcher. He is a Distinguished Lecturer of the IEEE Communications Society and a Fellow of IEEE.