Qian Yang

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut (Storrs). Her research focuses on computational science applied to physical sciences, particularly machine learning for materials, physics, and chemistry. She holds a Ph.D. in Computational and Mathematical Engineering from Stanford University and a B.A. in Applied Mathematics/Computer Science from Harvard College.

Before joining UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory Group. Her work emphasizes developing scalable models from molecular dynamics data, model reduction for nonlinear systems, and addressing challenges in scientific datasets (e.g., small, clustered, or imbalanced data).

• Research Interests: Machine Learning for Materials Science, Data-Driven Modeling, Uncertainty Quantification, Chemical Reaction Networks, and Multiscale Simulation.
• Awards: Best Instructor Award (Stanford, 2015/2016), Best Student/Postdoc Presentation Award (Materials Research Society, 2015).
• Teaching: Taught courses on Machine Learning for Physical Sciences, Computer Architecture, and computational methods at UConn and Stanford. Known for interdisciplinary approaches and student engagement.
• Labs/Teams: Leads research on computational modeling and hosts the Materials and Megabytes podcast, featuring interdisciplinary discussions with leading researchers.