I am a Professor in the Department of Industrial and Systems Engineering at University of Washington. I obtained B.S. degree in Industrial Engineering from Seoul National University in 2001. I received Ph.D. degree in Industrial Engineering from Texas A&M University, College Station, TX in 2011 under the guidance of Yu Ding in Industrial and Systems Engineering and Jianhua Huang in Statistics at Texas A&M University. In Fall 2016, I joined the Department of Industrial and Manufacturing Engineering at Florida State University as an Assistant Professor. I became an Associate Professor with tenure in 2017 and was promoted to a Professor in 2023. I moved to University of Washington in 2024.

My main research interest lies in machine learning and data science with applications to advanced manufacturing and physical science. I am particularly interested in modeling and analysis of object data. It concerns statistical analysis of complex objects and their visual features (such as image, shape, motion, function and directions). Object data are normally non-Euclidean features. Conventional statistical tools developed for Euclidean data do not apply here. Relevant research around object data is to define proper probability spaces of object data, and develop associated statistical inference algorithms. Many of my methodological studies are motivated by the problem of understanding processing-structure-property relations in manufacturing and physical sciences. In 2021, I authored the book Data Science for Nano Image Analysis, which summarizes many of my works in these areas. Another application is Data Science for Motion and Time Analysis in Operations Research. 

My more recent research is surrogate modeling of physical and computer experiments. I apply the surrogate works for creating digital twins in cyber-physical systems and developing AI-driven scientific discovery platforms. Gaussian processes has been the canonical choice for surrogate modeling of physical and computer experiments. However, they are not great modeling choices for nonstationarity, regime changes, and discontinuity, which are prevailing in many of my application systems. I have recently started working on some alternative surrogates such as Jump Gaussian Process. I have recent works in optimizing experiments or data acquisition processes to learn the new surrogate, i.e., Active Learning of Jump Gaussian Process Surrogates. 

1. Park, C., Waelder, R., Kang, B., Maruyama, B., Hong, S., and Gramacy, R. (2023). Active learning of piecewise Gaussian process surrogates. Preprint available at https://arxiv.org/abs/2301.08789.

2. Park, C. (2022) Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions. Journal of Machine Learning Research. 23(278):1−37 (Paper) 

3. Park, C., Noh, S., & Srivastava, A. (2022). Data Science for Motion and Time Analysis with Modern Motion Sensor Data. Operations Research. 70(6):3217-3233 (Paper) 

4. Park, C. and Ding, Y. (2021) Data Science for Nano Image Analysis. Springer Nature. ISBN 978-3-030-72821-2 (Paper)

5. Park, C., & Apley, D. (2018) Patchwork Kriging for Large-scale Gaussian Process Regression. Journal of Machine Learning Research. 19(7): 1-43 (Paper) 

6. Park, C., Woehl, T. J., Evans, J. E., & Browning, N. D. (2015). Minimum Cost Multi-way Data Association for Optimizing Multitarget Tracking of Interacting Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 611-624 (Paper)

7. Park, C. (2014). Estimating Multiple Pathways of Object Growth using Nonlongitudinal Image Data. Technometrics, 56(2), 186-199 (Paper) 

8. Park, C., Huang, J. Z., Ji, J., & Ding, Y. (2013). Segmenting, Inference and Classification of Partially Overlapping Nanoparticles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 669-681 (Paper) 

9. Park, C., Huang, J. Z., & Ding, Y. (2010). A Computable Plug-in Estimator of Minimum Volume Sets for Novelty Detection. Operations Research, 58(5), 1469-1480 (Paper)