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Jonathan Yu-Meng Li

Li, Jonathan Yu-Meng
Associate Professor
B.Sc. (National Sun Yat-Sen University), M.A.Sc. (McMaster), Ph.D. (University of Toronto)
DMS 7105
613-562-5800 x 4668
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Jonathan Li holds a Ph.D. from the Mechanical and Industrial Engineering Department at the University of Toronto. He received his B.Sc degree in Physics from National Sun Yat-Sen University in 2003, and his M.A.Sc. degree in Computational Engineering and Science from McMaster University in 2008.

Research interests

Professor Li's research interests focus on business analytics, operations research, and financial engineering. At the centre of his work are risk management problems that involve quantifying and modelling risk. Robust solutions to these problems contribute to more reliable decisions, ones less prone to uncertainty from market behaviour, stock prices, supply and demand characteristics, and other hard-to-predict phenomena. To this end, professor Li seeks to better understand and control uncertainty, using tools such as optimization algorithms and statistical learning. He has a particular interest in the area of financial engineering and his current projects tackle portfolio management, derivative pricing, and risk hedging. He is also involved in supply chain management projects.

Publications during the last 7 years

Papers in Refereed Journals

  • Cai, J., Li, J.Y. and Mao, T. 2023. Distributionally robust optimization under distorted expectations. Operations Research, (Accepted).
  • Marzban, S., Delage, E. and Li, J.Y. 2023. Deep reinforcement learning for equal risk pricing and hedging using dynamic expectile risk measures. Quantitative Finance, (Accepted).
  • Marzban, S., Delage, E. and Li, J.Y. 2022. Equal risk pricing and hedging of financial derivatives with convex risk measures. Quantitative Finance, 22(1): 47-73.
  • Li, J.Y. 2021. Inverse optimization of convex risk functions. Management Science, 67(11): 6629-7289.
  • Delage, E. and Li, J.Y. 2018. Minimizing risk exposure when the choice of a risk measure is ambiguous. Management Science, 64(1): 327-344.
  • Li, J.Y. 2018. Closed-form solutions for worst-case law invariant risk measures with application to robust portfolio optimization. Operations Research, 66(6): 1457-1759.

Funded Research during the last 7 years

Funded Research during the last 7 years
From-To Source Title * ** Role Amount
2023-2029 NSERC An analytic framework for the simultaneous pursuit of data­-drivenness and robustness in machine learning and optimization R C Co-PI $ 160,000
2023-2026 Telfer School of Management Research Grants (SMRG) Security and Privacy in a Decentralized Finance World R I PI $ 15,000
2022-2024 SSHRC Detection of Criminal Activity in Decentralized Finance R C Co-PI $ 24,889
2020-2023 NSERC Extension of Modeling and Optimization of Risk Measures R C PI $ 47,520
2020-2021 Mitacs (Brane Capital) A Deep Risk-Sensitive Reinforcement Learning Framework for Portfolio Management R O Co-PI $ 15,000
2019-2020 Mitacs (EVOVEST) Portfolio Management by Reinforcement Learning R O Co-PI $ 30,000
2014-2019 NSERC Modeling and Optimization of Risk Measures R C PI $ 110,000


C: Contract (R and D) | E: Equipment Grant | R: Research Grant | S: Support Award | P: Pedagogical Grant | O: Other, U: Unknown

C: Granting Councils | G: Government | F: Foundations | I: UO Internal Funding | O: Other | U: Unknown

PI = Principal Investigator | Co-I = Co-Investigator | Co-PI = Co-Principal Investigator

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