Department of Computer Science faculty candidate Dr. Wenbin Zhang, postdoctoral associate, Carnegie Mellon University, will present a virtual lecture on Thursday, March 31, at 1:00 p.m. via Zoom online meeting. Zhang’s research investigates the theoretical foundations of machine learning with a focus on societal impact and welfare.
Talk Title: Fairness and Graph Deep Generation through the Lens of Time
Talk Abstract: Many problems in machine learning are time-dependent in nature, which brings unique and complicated challenges such as uncertainty on the event of interest and the mutual interactions among static and dynamic patterns. To overcome these challenges, devising new techniques becomes essential. In this talk, I will be showing some of these new techniques through some machine learning problems I have recently worked on, such as fairness under uncertainty to support social fairness, and a generic framework of factorized deep generative models for interpretable dynamic graph generation, which provide necessary complements to these important yet challenging tasks.
Speaker Bio: Wenbin Zhang is a Postdoctoral Associate at Carnegie Mellon University, and an Associate Member at the Te Ipu o te Mahara Artificial Intelligence Institute. He received his Ph.D. from the University of Maryland, Baltimore County, and has been a visiting researcher at various global research centers and institutions. His research investigates the theoretical foundations of machine learning with a focus on societal impact and welfare. Other interests include deep generative models and health informatics with an academic track record across computer science and interdisciplinary venues, such as IJCAI, ICDM, AAAI, SDM, Climate Dynamics as well as Radiotherapy and Oncology. Visit Zhang’s GitHub profile.