معرفی
Zhe Shandian is an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on advancing machine learning methodologies, particularly in physics-informed machine learning, operator learning, and Bayesian methods. He explores applications in differential equations, multi-omics data, and engineering simulations. His work emphasizes scalable algorithms and data efficiency in complex systems.
Education details are not explicitly provided in the text, but his professional trajectory aligns with a strong computational and mathematical background. Research interests include developing novel frameworks for neural operators, symbolic regression, and functional Bayesian decomposition.
Key research trends from his articles highlight advancements in physics-guided AI models, multi-fidelity simulation, and scalable kernel-based methods. His contributions span domains like material science, cancer prediction, and traffic flow modeling. No scientific awards are mentioned in the provided text.
Zhe Shandian has not listed any advisees, and specific grants or labs are not detailed here. His work often involves collaboration across disciplines, leveraging both theoretical and applied machine learning techniques.
