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[Seminar] Fairness and Graph Deep Generation through the Lens of Time

Friday, March 11, 2022

11:00 am - 12:00 pm

Speaker

Wenbin Zhang
Postdoctoral Associate
Carnegie Mellon University

Location

Virtual via Zoom*
*Microsoft 365 @cougarnet.uh.edu authentication required to join via Zoom

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.

About the Speaker

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.

March 11 seminar