I am an Associate Professor in the Department of Industrial and Manufacturing Engineering at Florida A&M University - Florida State University. 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 A&M University - Florida State University as an Assistant Professor. I became an Associate Professor with tenure in 2017.

My main research interest lies in machine learning and data science with applications to advanced manufacturing and materials 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 over Riemannian manifolds and develop associated statistical inference algorithms. Many of my methodological studies are motivated by the problem of understanding processing-structure-property relations in materials science. 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 other research is AI-driven Scientific Discovery with focus on modeling of physical and computer experiments and active learning (or sequential adaptive design) for effective exploration of large dimensional experimental spaces for important scientific findings. Gaussian process surrogate modeling of experiments has been popular modeling choices. However, the GP modelings are not great modeling choices when the response surfaces of experiments are expected to be discontinuous. I have recently started working on some alternative modeling approaches such as jump regression analysis and related experimental design (or active learning) strategies.

1. Park, C. (in press) Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions. Journal of Machine Learning Research.

2. Park, C., Noh, S., & Srivastava, A. (in press). Data Science for Motion and Time Analysis with Modern Motion Sensor Data. Operations Research (Paper)

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

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

5. 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)

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

7. 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)

8. 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)