معرفی
Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut, Storrs. Her research focuses on machine learning applications in materials science, physics, and chemistry, bridging computational methods with physical sciences. She holds a Ph.D. from Stanford University's Institute for Computational and Mathematical Engineering and a B.A. in Applied Mathematics/Computer Science from Harvard College. Prior to UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory group.
Her research projects include developing machine learning models for chemical reaction networks, model reduction of nonlinear dynamical systems, and addressing challenges in scientific data (small datasets, imbalance, mixed fidelity). She co-hosts the Materials and Megabytes podcast, exploring interdisciplinary machine learning in materials science, featuring experts like Prof. Gábor Csányi (Cambridge) and Dr. Patrick Riley (Google).
- Awards: Best Student/Postdoc Presentation Award (2017), Best Instructor Award (Stanford)
- Teaching: Teaches courses like CSE 5095 (Machine Learning for Physical Sciences) and CSE 3666 (Computer Architecture). Known for integrating theory with practical applications.
- Grants/Advising: No explicit grants listed, but her research involves DOE and computational initiatives. Advising details not provided in text.
- Labs/Teams: Leads interdisciplinary teams focusing on machine learning in materials and physical sciences, as evidenced by podcast collaborations and course projects.
Publications emphasize data-driven methods, uncertainty quantification, and algorithm development for scientific challenges, with recent work in defect analysis, battery materials discovery, and TEM data automation.
