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Jonathan Yu-Meng Li

Li, Jonathan Yu-Meng
Associate Professor
RBC Financial Group Professorship in Financial Risk Analytics
B.Sc. (National Sun Yat-Sen University), M.A.Sc. (McMaster), Ph.D. (University of Toronto)
Location
DMS 7105
Telephone
613-562-5800 x 4668
Email
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Website
jonli.net/

Biography

Jonathan Li is an Associate Professor of Analytics and the Royal Bank of Canada (RBC) Financial Group Professor in Financial Risk Analytics at the Telfer School of Management, University of Ottawa. He serves as the Principal Coordinator of the Center for Responsible Wealth Transition and leads the Risk Intelligence and Resilient Solutions cluster. His research is supported by NSERC, SSHRC, and Mitacs, and has been published in flagship journals such as Management Science and Operations Research.

He spearheads Telfer’s Business Analytics Master’s program and teaches courses in Predictive Analytics, Financial Risk Management, Multivariate Research Methods, and Foundations for Quantitative Methods at the master's level, as well as Advanced Quantitative Analysis and Optimization at the PhD level.

Specializing in quantitative and algorithmic methodologies, Jonathan develops cutting-edge optimization and machine learning tools that drive data-driven decision-making in complex, evolving environments. His work empowers decision-makers to navigate uncertainty and optimize outcomes across sectors, including finance (e.g., portfolio management, asset pricing, fraud detection, and regulatory compliance), operations management (e.g., capacity planning, inventory management, and revenue management), and healthcare (e.g., patient triage and clinical risk assessment). His research focuses on managing market, credit, operational, and emerging risks driven by hard-to-predict phenomena such as market and individual behaviour, climate change, technology disruption, and supply and demand dynamics.

As the RBC Financial Group Professor in Financial Risk Analytics, his research program focuses on creating intelligent risk management solutions through cutting-edge methodologies in financial econometrics, optimization, and advanced machine learning, including foundational models, large language models, and deep reinforcement learning.

He holds a PhD in Operations Research (Financial Engineering specialization) from the University of Toronto, where his dissertation won first place at the CORS Paper Competition and was a finalist for the INFORMS Financial Services Section Paper Competition. He is a recipient of the Emerging Researcher Award at Telfer.

Publications during the last 7 years

Papers in Refereed Journals

  • Cai, J., Li, J.Y. and Mao, T. 2024. Distributionally robust optimization under distorted expectations. Operations Research, (In Press).
  • Marzban, S., Delage, E. and Li, J.Y. 2023. Deep reinforcement learning for option pricing g and hedging under dynamic expectile risk measures. Quantitative Finance, 23(10): 1411-1430.
  • Marzban, S., Delage, E., Li, J.Y., Desgagne-Bouchard, J. and Dussault, C. 2023. WaveCorr: deep reinforcement learning with permutation-invariant policy networks for portfolio management. Operations Research Letters, 51(6): 680-686.
  • 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.
  • 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.
  • 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.

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 PI $ 160,000
2023-2026 Telfer School of Management Research Grants (SMRG) Security and Privacy in a Decentralized Finance World R I Co-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

LEGEND:

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

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

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

Pillars
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