Welcome
Background
I am currently a postdoctoral fellow at the University of Toronto, Department of Mechanical and Industrial Engineering, where I focus on Markov Decision Processes and the application of approximate linear programming to improve policy approximations. I work under the supervision of Dr. Vahid Sarhangian, Dr. Andre Cire, and Dr. Adam Diamant. Previously, I was a Ph.D. candidate in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering at Texas A&M University, where I specialized in operations research and public health disease mitigation strategies under the supervision of Dr. Hrayer Aprahamian.
Research Interests
My research is situated at the intersections of operations research methodologies, healthcare systems, and public policy decision-making. I develop optimization-based frameworks that integrate individual-level data, dynamic behavioral changes, and fairness considerations to design efficient and equitable disease mitigation strategies. My research interests also include Markov Decision Processes, approximate linear programming, robust optimization, data science, and machine learning, which I leverage to model complex systems and enhance decision-making in both public health and engineering applications.
Award
I was recognized as the Outstanding Student in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering at Texas A&M University.
Education
- Ph.D. in Industrial Engineering, Texas A&M University, 2024
- M.S. in Industrial Engineering, Georgia Institute of Technology, 2018
- B.Eng. in Industrial Engineering, The Hong Kong Polytechnic University, 2017
Publications
Published Papers
- Li, S., & Aprahamian, H. (2024). An optimization-based framework to minimize the spread of diseases in social networks with heterogeneous nodes. IISE Transactions, 56(2), 128-142.
- Li, S., & Aprahamian, H. (2024). Quantifying the benefits of customized vaccination strategies: A network-based optimization approach. Naval Research Logistics (NRL), 71(1), 64-86.
- Li, S., Aprahamian, H., Nouiehed, M., & El-Amine, H. (2024). An Optimization-Based Order-and-Cut Approach for Fair Clustering of Data Sets. INFORMS Journal on Data Science, 3(2), 124-144.
- Barth, J., Li, S., Aprahamian, H., & Gupta, D. (2024). Spatiotemporal vaccine allocation policies for epidemics with behavioral feedback dynamics. Naval Research Logistics (NRL), 71(1), 109-139.
Submitted Papers / Working Papers
- Li, S., Aprahamian, H. & Chatterjee, S. (2024). A Convex Relaxation-Based Spatial Branching Approach for Optimal Robust Group Testing Designs under Prevalence Rate and Dilution Behavior Uncertainty. Third round revision at INFORMS Journal on Computing.
- Li, S., Lin, J., & Aprahamian, H. (2024). An Integrated Strategy for Controlling Infectious Disease Outbreaks: Social Distancing, Mass Screening, and Vaccine Distribution. Working paper.
- Lin, J., Li, S., & Aprahamian, H. (2024). A Multi-period Mass Screening Framework for the Optimal Control of Infectious Disease Outbreaks. Working paper.
Teaching Experience
Teaching Assistant
Texas A&M University, College Station, Texas (May 2022 - Dec 2023)
Courses:
- ISEN 620/320: Survey Optimization / Operation Research I
- ISEN 340: Operation Research II
- ISEN 302: Economic Analysis of Engineering Projects
- ISEN 230: Informatics for Industrial Engineers
Academic Service
Conference Session Chair
- Breaking the Chain: Using OR to Control Infectious Outbreaks, INFORMS Annual Meeting 2023
- Strengthening Healthcare Systems for Preparedness, INFORMS Annual Meeting 2024
- Health Application Society, INFORMS Anuual Meeting 2025
Journal Peer Reviewer
- INFORMS Journal on Data Science
- Health Care Management Science
- INFOR: Information Systems and Operational Research
- Journal of Supercomputing