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 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. 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.
- 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
| 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
