University of Virginia
Host: Cong Shen
Time and Location: Friday, October 2, 2020 2:00pm
Registration link: https://virginia.zoom.us/meeting/register/tJUuc-qopj8pHddomH0WSr36Uv65-ckxd2mB
Abstract: The ability to learn causality is considered as a significant component of human-level intelligence and can serve as the foundation of AI. In causality learning, one fundamental problem is to understand the causal effects of a specific treatment (e.g., prescription of medicine) on an important outcome (e.g., cure of a disease), with significant implications in various high-impact domains such as health care, education, and e-commerce. One prevalent way to solve the problem is to directly use the observational data since the alternative randomized experiments could be expensive, time-consuming, and even unethical in many scenarios. However, existing data-driven methods are often limited since they: (1) assume that observational data is independent and identically distributed (i.i.d.); and (2) ignore the influence of hidden confounders (i.e., the unobserved variables that affect both the treatment and the outcome). Meanwhile, real-world data is often connected and can be abstracted as graphs (e.g., social networks, biological networks, and knowledge graphs). The ubiquitous of graph data across many influential areas also brings opportunities to control the influence of hidden confounders and build more effective models that yield unbiased causal effects estimation. In this talk, I will introduce our recent research efforts in causal effects learning with graphs. Specifically, we attempt to answer the following research questions: How to utilize graph information among observational data for causal effects learning? How to harness the power of historical information to tame the influence hidden confounders for causal effects learning when the graph is continuously evolving?
Biography: Jundong Li is an Assistant Professor in the Department of Electrical and Computer Engineering, with a joint appointment in the Department of Computer Science, and the School of Data Science. He received Ph.D. degree in Computer Science at Arizona State University in 2019, M.Sc. degree in Computer Science at the University of Alberta in 2014, and B.Eng. degree in Software Engineering at Zhejiang University in 2012. His research interests are in data mining, machine learning, and causal inference. He has published more than 70 articles in high-impact venues (including KDD, WWW, AAAI, IJCAI, WSDM, CSUR, TKDE, TKDD, etc.), with over 2,000 citation count. His work on feature selection and graph representation learning are among the most cited articles in ACM CSUR, WSDM, SDM, and CIKM within the past five years according to Google Scholar Metrics. He regularly serves on (senior) program committees for major international conferences and reviews for reputable journals. He was the Sponsor Chair for WSDM 2020.