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Xun Huan

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Prof. Huan’s research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. He focuses on methods to bridge together models and data: e.g., optimal experimental design, Bayesian statistical inference, uncertainty propagation in high-dimensional settings, and algorithms that are robust to model misspecification. He seeks to develop efficient numerical methods that integrate computationally-intensive models with big data, and combine uncertainty quantification with machine learning to enable robust and reliable prediction, design, and decision-making.

Optimal experimental design seeks to identify experiments that produce the most valuable data. For example, when designing a combustion experiment to learn chemical kinetic parameters, design condition A maximizes the expected information gain. When Bayesian inference is performed on data from this experiment, we indeed obtain “tighter” posteriors (with less uncertainty) compared to those obtained from suboptimal design conditions B and C.

Ding Zhao

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Ding Zhao, PhD, is Assistant Research Scientist in the department of Mechanical Engineering, College of Engineering with a secondary appointment in the Robotics Institute at The University of Michigan, Ann Arbor.

Dr. Zhao’s research interests include autonomous vehicles, intelligent/connected transportation, traffic safety, human-machine interaction, rare events analysis, dynamics and control, machine learning, and big data analysis

 

Jingwen Hu

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Jingwen Hu, PhD, is Associate Research Scientist in the University of Michigan Transportation Research Institute (UMTRI) with a secondary appointment as Associate Research Scientist in Mechanical Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

The primary goal of Prof. Hu’s research is to reduce the incidence of injuries and fatalities in motor-vehicle crashes and other injurious events using a multidisciplinary approach.  It involves 1) collecting and analyzing large-scale injury data to identify the injury problems and assess the performance of safety designs, 2) performing physical tests and computational simulations to investigate human impact responses, injury mechanisms, and injury tolerances for various body regions under field-relevant loading conditions, and 3) developing tools for large-scale computational simulations to explore the best solutions for reducing impact-induced injuries.