Welcome to Qinxin’s homepage!

I am Qinxin Yan, a fifth-year PhD student in the Program in Applied and Computational Mathematics (PACM) at Princeton University. I am fortunate to be advised by Prof. H. Mete Soner in the Department of Operations Research and Financial Engineering (ORFE).

My research develops asymptotic mathematical frameworks for large stochastic and strategic systems, combining control and game theory to analyze and design decision mechanisms in multi-agent settings. I blend stochastic simulation, mean-field theory, and graph-theoretic modeling with machine-learning–based algorithms to connect rigorous theory with applications in real-world problems, especially in finance and machine learning.

During 2023–2024, I was as a visiting scholar with the Insurance Mathematics and Stochastic Finance Group (Group 3) in the Department of Mathematics at ETH Zurich. There, I worked with Prof. Josef Teichmann on machine learning algorithms for Mckean-Vlasov control, and with Prof. Beatrice Acciaio on the mean field formulation of large neural networks.

My current projects include:

• Viscosity solutions of Hamilton–Jacobi equations arising in Mckean-Vlasov control.
• Machine-learning-based methods for Mckean-Vlasov control problems and multi-agent games.
• Connections between Mckean-Vlasov control, Wasserstein gradient flows, and optimal transport.
• Applications of mean field theory and Wasserstein gradient flows in large-scale neural networks.
• Large stochastic particle systems with singular interactions on sparse graphs, with applications to systemic risk in financial networks.

Please explore my reseach to learn more.

I am on the job market for fall 2025.