About me

I am a Postdoc Research Fellow in the area of Operations Management and Statistics at the Joseph L. Rotman School of Management, University of Toronto, under the supervision of Ningyuan Chen, Ming Hu and Sheng Liu. I received my PhD in applied math (AMSC program) from the University of Maryland, College Park. I was fortunate to be advised by Ilya Ryzhov.

My research interest broadly lies in (i) understanding unexpected outcomes narrated with estimation and inference involving (insufficient) observational data, and (ii) developing methodologies for better decision-making under uncertainty through improved data utilization, with both topics covering applications with the theme of societal good such as problems in privacy and experimental design. Technically, my research is positioned at the interface of stochastic modeling and uncertainty quantification.

Publications

Authors in alphabetical order.

• Jialin Li, and Ilya Ryzhov, “Moderate deviations inequalities for Gaussian process regression.” Journal of Applied Probability 61(1): 172-197. link
• Jialin Li, and Ilya Ryzhov, “Convergence rates of epsilon-greedy global optimization under radial basis function interpolation.” Stochastic Systems 13(1): 59-92. link
  • Supported by Graduate Student Summer Research Fellowship (5000USD), University of Maryland, 2019

Working Papers

• Ningyuan Chen, Ming Hu, Jialin Li, and Sheng Liu, “Data privacy in pricing: Estimation bias and implications.” To be soon submitted. link
  • Supported by TD Management Data and Analytics Lab Research Grant (4000CAD), Rotman School of Management, 2023
  • Supported by New Pilot Postdoc Funding (2000CAD), Rotman School of Management, 2022

Work in Progress

• Furong Huang, Jialin Li, and Xuchen You, “Guaranteed simultaneous asymmetric tensor decomposition via alternating subspace iteration.” To be submitted. link
• Ningyuan Chen, Ming Hu, Jialin Li, and Sheng Liu, “Incentivizing greedy customers to explore.”