Peng Chen
Assistant Professor
Dr. Chen is an Assistant Professor in the School of Computational Science and Engineering. Previously he was a Research Scientist at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. Dr. Chen’s research is in the multidisciplinary fields of computational mathematics, data science, scientific machine learning, and parallel computing with various applications in materials, energy, health, and natural hazard. Specifically, his research focuses on developing fast, scalable, and parallel computational methods for integrating data and models under high-dimensional uncertainty to make (1) statistical model learning via Bayesian inference, (2) reliable system prediction with uncertainty quantification, (3) efficient data acquisition through optimal experimental design, and (4) robust control and design by stochastic optimization.
Office Location:
CODA | E1350B
Scientific Machine Learning (SciML) and Uncertainty Quantification (UQ)
- Bayesian Inference
- Infectious Diseases
- Optimal Experimental Design
- Plasma Fusion
- Stochastic Optimization
- Uncertainty Quantification
IRI Connection:
