(734) 834-4191

Applications:
Industrial Engineering, Operations Research, Transportation Research
Methodologies:
Artificial Intelligence, Bayesian Methods, Classification, Deep Learning, Image Data Processing and Analysis, Machine Learning, Predictive Modeling, Real-time Data Processing, Signal Processing, Spatio-Temporal Data Analysis, Statistical Inference, Statistical Modeling, Tensor Analysis, Time Series Analysis
Relevant Projects:

NSF, Ford, GM


Wenbo Sun

Assistant Research Scientist

University of Michigan Transportation Research Institute

Uncertainty quantification and decision making are increasingly demanded with the development of future technology in engineering and transportation systems. Among the uncertainty quantification problems, Dr. Wenbo Sun is particularly interested in statistical modelling of engineering system responses with considering the high dimensionality and complicated correlation structure, as well as quantifying the uncertainty from a variety of sources simultaneously, such as the inexactness of large-scale computer experiments, process variations, and measurement noises. He is also interested in data-driven decision making that is robust to the uncertainty. Specifically, he delivers methodologies for anomaly detection and system design optimization, which can be applied to manufacturing process monitoring, distracted driving detection, out-of-distribution object identification, vehicle safety design optimization, etc.