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 2011, 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, opening the Digital Transformation Lab.

My primary research interests lie in machine learning and data science, with applications in advanced manufacturing and the physical sciences. I am particularly focused on the modeling and analysis of object data, which involves the statistical study of complex structures and their visual features—such as images, shapes, motion, functions, and directions. Unlike traditional numerical data, object data are typically non-Euclidean, meaning that conventional statistical tools developed for Euclidean (or vector) spaces are not directly applicable. Research in this area involves defining appropriate probability spaces for object data and developing corresponding statistical inference algorithms. Recently, our efforts have centered on modeling and analyzing human performance. Using a shape-theoretic approach, we model human performance as a time series of body postures. Our initial work in this direction is presented in the paper, Data Science for Motion and Time Analysis in Operations Research, with more studies currently underway. Many of my methodological studies in this direction are also 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 area of my research focuses on surrogate modeling for physical and computer experiments. Surrogate models are used to replace computationally expensive simulations and to better understand discrepancies between simulation results and real-world observations—ultimately enabling the calibration of computer models by estimating and accounting for these gaps. Gaussian Processes (GPs) have long been the standard approach for surrogate modeling in this domain. However, traditional GPs are not well-suited for handling nonstationarity, regime changes, discontinuities, and categorical input variables—all of which are common in many of my application areas. To address these limitations, I have recently begun exploring alternative surrogate models, such as Jump Gaussian Process. My recent work includes optimizing experimental designs and data acquisition strategies for learning these new surrogates, specifically through, 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)