Haoyang Cao

Haoyang Cao is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, affiliated with the Data Science and AI Institute. Her research focuses on stochastic controls, stochastic differential games, mean-field games, and theoretical machine learning paradigms such as generative models and transfer learning. She bridges stochastic analysis with financial mathematics applications, including portfolio optimization and inventory control. Cao holds a PhD from UC Berkeley (2020) and a BS from the University of Hong Kong (2015). Prior to joining JHU, she was a postdoc at École Polytechnique (2022–2023), a research associate at the Alan Turing Institute (2020–2021), and a visiting scholar at the University of Oxford's Mathematical Institute.

Her education includes:

• PhD in Industrial Engineering and Operations Research, UC Berkeley (2020)
• Bachelor of Science in Mathematics, University of Hong Kong (2015)

Research interests emphasize high-dimensional stochastic games and machine learning applications, particularly in finance and healthcare. She develops computational tools for solving complex stochastic systems and explores theoretical foundations of generative models and transfer learning. Recent work includes analyzing risk in transfer learning, approximation techniques in mean-field games, and anomaly detection via meta-learning.

Her work on stochastic controls and games has been applied to financial mathematics, inventory control, and healthcare systems. Key contributions include frameworks for transfer learning feasibility and GAN training dynamics analysis.

Labs/Teams: Active member of the Data Science and AI Institute at Johns Hopkins University, collaborating on interdisciplinary projects combining stochastic analysis and machine learning.