University of Virginia
Qingyun Wu is a 5-th year Ph.D. candidate advised by Dr. Hongning Wang in the department of computer science, University of Virginia. Her research focuses on interactive online learning, sequential decision optimization and their applications to a wide spectrum of scenarios, such as information retrieval systems and data exploration tasks. In addition to her graduate studies, she interned at Yahoo Research, Adobe research and Microsoft research. Qingyun’s research has been published in top-tier machine learning and information retrieval conferences. She won the graduate research award for outstanding research in 2017 and 2018 at the University of Virginia and was the recipient of the Virginia Engineering Foundation Fellowship of 2018. She won the best graduate short at the 2017 ACM Capital Region Celebration of Women in Computing Conference. Her joint work with her colleagues at UVa on online learning to rank won the best paper award of SIGIR 2019.
Title: Interactive Online Learning for Intelligent Systems
The past several years have witnessed a growing need for intelligent systems that work in real-time to satisfy people’s various needs. Most existing learning solutions in such systems are passive and offline. However, due to the heterogeneity and dynamic nature of users, a generic offline trained algorithm can hardly satisfy each individual user’s need, which calls for interactive online learning solutions. Online learning solutions explore the unknowns by sequentially collect individual user’s feedback. It helps address the notorious explore/exploit dilemma during sequential decision making. My research goal is to build a new interactive online learning paradigm for information service systems. This interactive online learning paradigm can be applied to a wide spectrum of applications, including modern recommender systems, interactive online education systems, human-in-the-loop cyber-physical systems and many more.