Quan Zhang

Quan Zhang is an Assistant Professor in the Department of Accounting and Information Systems at the Broad College of Business, Michigan State University. With a Ph.D. in Information, Risk and Operations Management from the University of Texas at Austin (2020), an M.S. in Biostatistics from the University of Minnesota (2015), and a B.S. in Biological Science and Economics from Peking University (2012), his academic journey bridges diverse disciplines.

• Education
  • Ph.D., Information, Risk and Operations Management - University of Texas at Austin (2020)
  • M.S., Biostatistics (Ph.D. study) - University of Minnesota at Twin Cities (2015)
  • B.S., Biological Science and Economics - Peking University (2012)

His research focuses on interpretable machine learning, particularly balancing model interpretability and capacity using Bayesian and variational inference methods. Applications span telemedicine, quantitative marketing, medical data analysis, and online finance, with recent work on watermarking for copyright protection and robust representation learning.

Selected publications include his 2025 paper on Beyond Invisibility: Learning Robust Visible Watermarks, 2023 work on Weibull Racing Survival Analysis, and foundational 2018 contributions to Nonparametric Bayesian Lomax Delegate Racing. His methodological innovations in Bayesian multinomial regression and survival modeling have been published in Advances in Neural Information Processing Systems and the Journal of Machine Learning Research.

He contributes to software development with Python and R implementations for Bayesian models, available in his GitHub repository. His teaching includes graduate-level ITM885 Machine Learning and Optimization, emphasizing practical applications in business analytics.