Jangwon Park

Jangwon Park

PhD student in operations research

About Me

Hi! My name is Jang. I am a 5th year PhD student in Operations Research at the University of Toronto. My advisors are Timothy Chan and Vahid Sarhangian.

My research develops data-driven insights and policies to help health systems become more resilient under demand uncertainty and resource scarcity, drawing on stochastic modeling, causal inference, and algorithm design. I work extensively with large clinical datasets and collaborate closely with practitioners to inform modeling and analysis. Currently, I focus on optimizing inter-hospital patient transfer decisions from both empirical and theoretical perspectives.

Before joining U of T, I completed MSc. in Business Analytics at EPFL in 2020, and was a research scientist on Swissgrid’s Research and Digitalization team from March-Oct 2020. I completed BASc. in Engineering Science at U of T in 2018.

Contact: jangwon.park [at] mail.utoronto.ca

Working Papers

6. Causal impact of inter-hospital patient transfers
with Carri Chan, Timothy Chan, and Vahid Sarhangian
Work in progress, 2025

5. Dynamic transfer policies for parallel queues [Preprint]
with Timothy Chan and Vahid Sarhangian
Major revision, Operations Research, 2024
      Second place, 2024 CORS Queueing and Applied Probability SIG Student Paper Competition

4. Optimizing inter-hospital patient transfer decisions: a queueing network approach [Preprint]
with Timothy Chan, Frances Pogacar, and Vahid Sarhangian
Major revision, Manufacturing & Service Operations Management, 2024
     First place, 2024 CORS Healthcare SIG Student Paper Competition
     Accepted for presentation, 2025 MSOM Healthcare SIG

3. Robust confidence bands for stochastic processes using simulation [Preprint]
with Timothy Chan and Vahid Sarhangian
Minor revision, Operations Research Letters, 2024
      Accepted for presentation, 2024 Winter Simulation Conference PhD Colloquium

Published Papers

2. Evolution of the surgical procedure gap during and after the COVID-19 pandemic in Ontario, Canada: a cross-sectional and modeling study [DOI]
with Timothy Chan, Vahid Sarhangian, and Rachel Stephenson
British Journal of Surgery, 2023

1. Trends in short-term renewable and load forecasting for applications in smart grid [DOI]
with Deepa Kundur and Dongchan Lee
Smart City 360, 2016

Industry Projects

2. Automating Order Management Process at Daily Bread Food Bank
with Helen Lee, Rachel Wong
Daily Bread Food Bank, Toronto, Canada, 2025

1. A hybrid optimization approach for employee rostering: Use cases at Swissgrid and lessons learned [arXiv]
with Evangelos Vrettos
Swissgrid Ltd., Aarau, Switzerland, 2021

Presentations

  • Applied Probability Society: 2025
  • MSOM: 2025
  • Winter Simulation Conference: 2024
  • INFORMS Annual Meeting: 2022, 2023, 2024
  • CORS Annual Conference: 2024, 2025
  • INFORMS Healthcare Conference: 2023

Teaching

As an instructor:

  • MIE 368: Analytics in Action (2024), Instructor Rating: 4.5/5
    Third-year undergraduate class in applied analytics, University of Toronto.

As a teaching assistant:

  • MIE 368: Analytics in Action (2022, 2023), Average TA Rating: 4.8/5
    Third-year undergraduate class in applied analytics, University of Toronto.

  • MIE 1613: Stochastic Simulation (2023, 2024)
    Graduate class in stochastic modeling and simulation, University of Toronto.

  • Volunteer Instructor for Statistics Without Borders (2023)
    Topic: Machine learning (link to online video lessons)

  • MGOD 31: Advanced Business Data Analytics (2022)
    Third-year undergraduate class in applied machine learning, University of Toronto Scarborough.

Awards

Service

Ad-hoc journal referee:

  • Operations Research
  • Health Care Management Science

Conference session chair:

  • CORS Annual Conference 2025
  • INFORMS Annual Meeting 2024
  • INFORMS Healthcare Conference 2023