Xinyuan Zhang

Welcome to my website!

I am an Assistant Professor in the Operations and Decision Sciences area at the Lazaridis School of Business and Economics, Wilfrid Laurier University. As a researcher, I aim to utilize statistical learning and stochastic control methods to address challenges inherent in data-driven decision-making, especially within service operations and revenue management applications. My recent research has focused on improving decision-making in complex information-sharing environments. (e.g., misinformation, strategic behaviors). Details can be found here.

I received my PhD in Management Science from the Sauder School of Business, University of British Columbia, where I was advised by Prof. Michael Jong Kim. Before that, I earned a Master of Applied Science in Operations Research from the University of Toronto and a Bachelor of Science in Physics and Mathematics from McGill University.

You can find my CV here.

Below, you’ll discover information about my recent talks and what’s upcoming.

news

Oct 24, 2025	Talk @ INFORMS Annual Meeting

selected publications

2022

OR
Learning Manipulation Through Information Dissemination

J. Keppo, M. Kim, and X. Zhang

Operations Research, 2022

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We study the optimal manipulation of a Bayesian learner through adaptive information provisioning, motivated by firms employing false advertising on e-commerce platforms and social networks. Specifically, we consider a firm that disseminates biased information to manipulate public beliefs about a parameter related to its payoffs. In our sequential optimization model, the firm dynamically decides on both the quantity and content of information sent to the public to maximize its expected total profits. By solving the Bayesian dynamic programming equation, we explicitly characterize the optimal manipulation strategy and also the evolution of public belief under such manipulation over time. We also consider the public as partially Bayesian social learners who utilize public reviews to mitigate manipulation. We show that the public eventually learns the truth from misinformation using social learning.
@article{keppoetal2023learnmanip, title = {Learning Manipulation Through Information Dissemination}, author = {Keppo, J. and Kim, M. and Zhang, X.}, journal = {Operations Research}, volume = {70}, number = {8}, pages = {3490-3510}, year = {2022} }