Xinlun Cheng

Xinlun Cheng is a Postdoctoral Research Associate in the School of Data Science at the University of Virginia, working in Professor Stephen Baek's Visual Intelligence Lab on physics-informed machine learning (PIML) and Physics-aware Recurrent Convolutional neural networks (PARCv2).

His educational background includes:

• Ph.D. in Astronomy, University of Virginia
• M.S. in Data Science, University of Virginia
• B.S. in Physics, Tsinghua University

Cheng specializes in developing PIML algorithms and training strategies, with extensive experience applying PARCv2 to computational fluid dynamics problems and comparing model performance. His interdisciplinary work bridges data science and astronomy through large sky survey database mining with the UVA Department of Astronomy.

The Visual Intelligence Lab serves as his primary research environment where he continues advancing PARCv2 frameworks for scientific machine learning applications.