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Xun Huan

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Xun (Ryan) Huan is an Assistant Professor in the Department of Mechanical Engineering. His research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. His expertise focuses on bridging models and data: optimal experimental design, Bayesian methods for statistical inference, uncertainty propagation in high-dimensional settings, and methods that are robust to model misspecification. He seeks to develop efficient computational methods that integrate realistic models with big data, and combine uncertainty quantification with machine learning to enable robust prediction, design, and decision-making. He is interested in collaborative opportunities in various applications that can benefit from a better understanding of uncertainty and modeling. Current research activities include assessing uncertainty in deep neural networks, and developing sequential experimental design methods for improving autonomy.

Optimal experimental design seeks to identify experiments that produce the most valuable data, and can lead to substantial resource savings. For example, in the design of a shock-tube combustion experiment, design condition A maximizes the expected information gain. When Bayesian inference is performed on data from this experiment, we obtain “tighter” posteriors (with less uncertainty) compared to those obtained from suboptimal design conditions B and C.