Jonathan Yu-Meng Li
- Location
- DMS 7105
- Telephone
- 613-562-5800 x 4668
This email address is being protected from spambots. You need JavaScript enabled to view it. - Website
- jonli.net/
Biography
Jonathan Li is an Associate Professor of Analytics and holder of the RBC Financial Group Professorship 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
| 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
