Haoyang Cao
About me
I am an assistant professor at the Department of Applied Mathematics and Statistics of Johns Hopkins University, starting from January 2024. I have worked as a postdoctoral researcher at Centre de Mathématiques Appliquées (CMAP), École Polytechnique from January 2022 to December 2023 under the supervision of Mathieu Rosenbaum. Before joining CMAP, I have been working as a Machine Learning in Finance Research Associate at the Alan Turing Institute in London under the supervision of Prof. Samuel N. Cohen and Prof. Lukasz Szpruch from September 2020 to December 2021. During my stay in the UK, I have also been a visiting scholar at the Mathematical Institute at University of Oxford hosted by Prof. Rama Cont and Prof. Cohen. I completed my PhD study in 2020 under the supervision of Prof. Xin Guo in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Prior to my PhD study, I received my Bachelor of Science degree in mathematics from the University of Hong Kong in 2015.
Research
My research interests can be roughly divided into two categories. One is about stochastic differential games and mean field games, together with related topics including applied probability, stochastic analysis, partial differential equations and applications in finance & operations research. The other is machine learning and its applications in engineering & finance.
Haoyang Cao, Xin Guo and Mathieu Lauriere. Connecting GANs, mean-field games and optimal transport. To appear, SIAM Journal on Applied Mathematics, 2024.
Haoyang Cao and Xin Guo. SDE approximations of GANs training and its long-run behavior. Journal of Applied Probability (First View), pp. 1-25, 2023.
Haoyang Cao and Xin Guo. Generative Adversarial Networks: Some Analytical Perspective. Machine Learning for Financial Markets: a guide to contemporary practices, edited by Agostino Capponi and Charles-Albert Lehalle, Cambridge University Press, 2023.
Haoyang Cao, Xin Guo and Joon Seok Lee. Approximation of N-player stochastic games with singular controls by mean field games. Numerical Algebra, Control and Optimization 13(3&4), 2023.
Haoyang Cao, Jodi Dianetti, and Giorgio Ferrari. Stationary discounted and ergodic mean field games with singular controls. Mathematics of Operations Research 48(4), pp. 1871-1893, 2022.
Matteo Basei, Haoyang Cao, and Xin Guo. Nonzero-sum stochastic games and mean-field games with impulse controls. Mathematics of Operations Research 47(1), pp. 341-366, 2022.
Haoyang Cao and Xin Guo. MFGs for partially reversible investment. Stochastic Processes and their Applications, Vol. 150, pp. 995-1014, 2022.
Haoyang Cao, Samuel N. Cohen and Lukasz Szpruch. Identifiability in inverse reinforcement learning. Advances in Neural Information Processing Systems 34, 2021.
Haoyang Cao, Haotian Gu, Xin Guo, and Mathieu Rosenbaum. Risk of transfer learning and its applications in finance. Submited, 2023.
Qinkai Chen, Mohamed El-Mennaoui, Antoine Fosset, Amine Rebei, Haoyang Cao, Philine Bouscasse, Christy Eóin O'Beirne, Sasha Shevchenko, and Mathieu Rosenbaum. Towards mapping the contemporary art world with ArtLM: an art-specific NLP model. Submitted, 2023.
Haoyang Cao, Haotian Gu, Xin Guo. Feasibility and transferability of transfer learning: a mathematical framework. Working paper ,2024.
Haoyang Cao, Xin Guo and Guan Wang. Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system. Working paper, 2024.
Guan Wang, Yusuke Kikuchi, Haoyang Cao, Jinglin Yi, Qiong Zou, Rui Zhou, and Xin Guo. Transfer learning for retinal vascular disease detection: a pilot study with diabetic retinopathy and retinopathy of prematurity. Working paper, 2024.
Talks
ICAIF’23 Workshop – Transfer Learning and its Applications in Finance, New York NY, Nov 2023
Paris Bachelier Seminar, Paris France, Nov 2023
2023 INFORMS Annual Meeting, Phoenix AZ, Oct 2023
ICAIM 2023, hybrid, Aug 2023
IMSI workshop on Machine Learning and Mean-Field Games, Chicago IL (hybrid), May 2022
Stochastic Control & Analysis and Applications, Hammamet Tunisia, Mar 2022
Paris Bachelier Seminar, Paris France, Feb 2022
2021 SIAM Conference on Financial Mathematics and Engineering, virtual, June 2021
2020 INFORMS Annual Meeting, virtual, Nov 2020
Oxford Data Science Seminar, Nov 2020
2019 INFORMS Annual Meeting, Seattle WA, Oct 2019
Cornell ORIE Young Researchers Workshop, Ithaca NY, Oct 2019
Equilibria in Markets, Strategic Interactions, and Complex Systems, Bielefeld Germany, July 2019
9th General AMaMeF Conference, Paris France, June 2019
2018 INFORMS Annual Meeting, Phoenix AZ, Nov 2018
Events
ICAIF'23 Workshop -- Transfer Learning and its Applications in Finance, co-organizer, New York, Nov 2023
Uncertainty and Risk Workshop, co-organizer, virtual, Mar 2021
INFORMS APS Cluster Session "Bridging Deep Learning with Stochastic Analysis and Mean-Field Theory", co-chair, virtual, Nov 2020
Teaching
In the coming Spring semester of 2024, I will be teaching a newly approved course at Master/PhD level.
EN.553.640 Machine Learning in Finance
My past experiences as teaching assistant cover undergraduate to PhD level probability and stochastic processes, including topics such as random variables, Markov chain, Poisson process, renewal process, martingale, Brownian motion, Itô calculus, etc. I am also interested in offering courses in financial engineering, simulation and machine learning.
IEOR 263A Applied Stochastic Processes I, Fall 2016
IEOR 173 Introduction to Stochastic Processes, Spring 2017
IEOR 241 Risk Modeling, Simulation, and Data Analysis, Fall 2017, Fall 2018
IEOR 263B Applied Stochastic Processes II, Spring 2018, Spring 2019
IEOR 221 Introduction to Financial Engineering, Fall 2019
IEOR 222 Financial Engineering Systems I, Spring 2020