Jing An

Jing An is an Assistant Research Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. She holds a Ph.D. from Stanford University (2021) and completed her postdoctoral research at the Max Planck Institute for Mathematics in the Sciences (2021–2022). Her research focuses on mathematical foundations of machine learning and data science, employing tools from stochastic analysis and partial differential equations (PDEs), with additional expertise in reaction-diffusion equations and evolution systems.

Education:

• Ph.D., Mathematics, Stanford University (2021)
• B.S., Mathematics, University of California, Los Angeles (2016)

Research Interests:
Jing’s work bridges stochastic processes and PDEs with modern machine learning challenges. Key areas include convergence analysis of optimization algorithms (e.g., stochastic gradient descent), probabilistic interpretations of PDE solutions, and front propagation dynamics in reaction-diffusion systems. She has developed novel frameworks for understanding voting models through branching Brownian motion and explored quantitative steepness in semi-FKPP reactions.

Grants:

• Simons Travel Grant (Principal Investigator, 2023–2025)

Teaching: Recent courses include Mathematical Numerical Analysis (Spring 2025), Ordinary and Partial Differential Equations, and multiple offerings of Elementary Differential Equations. She emphasizes computational methods and theoretical rigor in her instruction.

Labs/Teams: While no specific lab is mentioned, her research collaborates with leading institutions like the Max Planck Institute and Duke’s Mathematics Department, focusing on interdisciplinary mathematical problems.