Publications

Methodology Papers

  1. Park, C., Borth, D.J., Wilson, N.S. & Hunter, C.N. (In press) Variable selection for Gaussian process regression through a sparse projection. IISE Transactions. (Online First)

  2. Esmaieeli, A., & Park, C. (2021) A mixture of linear-linear regression models for linear-circular regression. Statistical Modelling. 21 (3): 220-243.

  3. Mu, C. & Park, C. (2020) Sparse filtered SIRT for electron tomography. Pattern Recognition. 102: 107253.

  4. Qian, Y., Huang, J., Park, C., Ding, Y. (2019) Fast dynamic nonparametric distribution tracking in electron microscopic data. Annals of Applied Statistics. 13(3): 1537-1563.

  5. Park, C., & Apley, D. (2018) Patchwork kriging for large-scale Gaussian process regression. Journal of Machine Learning Research. 19(7): 1-43.

  6. Esmaieeli, A., Welch, D. A., Woehl, T., Faller, R., Evans, J. E., Browning, N. D., & Park, C. (2018). Directional statistics of preferential orientations of two shapes in their aggregate and its application to study preferential attachment of nanoparticles. Technometrics. 60(3): 332-344

  7. Vo, G., & Park, C. (2018). Robust regression for image binarization under heavy noises and nonuniform background. Pattern Recognition. 81: 224-239.

  8. Li, X., Belianinov, A., Jesse, S., & Park, C. (2018). Two-level structural sparsity regularization for finding lattice locations and defects in noisy image data. Annals of Applied Statistics. 12(1): 348-377.

  9. Li, X., Tran, P., Liu, T., & Park, C. (2017). Simulation-guided regression approach for estimating the nanoparticle size distribution with dynamic light scattering data. IISE Transactions. 49(1), 70-83.

  10. Park, C., & Huang, J.Z. (2016). Efficient computation of Gaussian process regression for large spatial data sets by patching local Gaussian processes. Journal of Machine Learning Research. 17(174),1−29.

  11. Park, C., Woehl, T. J., Evans, J. E., & Browning, N. D. (2015). Minimum cost multi-way data association for optimizing large-scale multitarget tracking of interacting objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 611-624.

  12. Park, C. (2014). Estimating multiple pathways of object growth using non-longitudinal image data. Technometrics, 56(2), 186-199.

  13. Park, C., & Shrivastava, A. (2014). Multimode geometric profile monitoring with temporally correlated image data and its application to nanoparticle self-assembly processes. Journal of Quality Technology, 46(3), 1-32.

  14. 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.

  15. Park, C., Huang, J. Z., & Ding, Y. (2012). GPLP: A local and parallel computation tool box for Gaussian process regression. Journal of Machine Learning Research, 13, 775-779.

  16. Park, C., Huang, J. Z., Huitink, D., Kundu, S., Mallick, B., Liang, H., & Ding, Y. (2012). A multi-stage, semi-automated procedure for analyzing the morphology of nanoparticles. IIE Transactions, 7, 507-522.

  17. Park, C., Huang, J. Z., & Ding, Y. (2011). Domain decomposition for fast Gaussian process regression of large spatial datasets. Journal of Machine Learning Research, 12, 1697−1728.

  18. 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.

Review Papers

  1. Stach, E., DeCost, B., Kusne, A.G., Hattrick-Simpers, J., Brown, K.A., Reyes, K.G., Schrier, J., Billinge, S., Buonassisi, T., Foster, I., Gomes, C.P., Gregoire, J.M., Mehta, A., Montoya, J., Olivetti, E., Park, C., Rotenberg, E., Saikin, S.K., Smullin, S., Stanev, V., and Maruyama, B. (2021) Autonomous Experimentation Systems for Materials Development: A Community Perspective. Matter: Cell Press. 4(9), 2702-2726.

  2. Park, C. & Ding, Y. (2019) Automating material image analysis for material discovery. MRS Communications, 9 (2), 545-555.

  3. Agbabiaka, A., Wiltfong, M., & Park, C. (2013). Small angle X-ray scattering technique for the particle size distribution of nonporous nanoparticles. Journal of Nanoparticles, 2013, 11.

Application (Material Imaging)

  1. Bakalis, E., Parent, L.R., Park, C., Gianneschi, N. C., & Zerbetto F. (2020) Complex nanoparticle diffusional motion in liquid cell transmission electron microscopy. Physical Chemistry Chemical Physics. 124(27), 14881-14890.

  2. Touve, M., Wright, D., Mu, C., Park, C. & Gianneschi, N. (2019) Block copolymer amphiphile phase diagrams by high-throughput transmission electron microscopy. Macromolecules, 52 (1), 5529-5537.

  3. Wang, M., Dissanayake, T., Park, C., Gaskell, K. & Woehl, T. (2019) Nanoscale mapping of non-uniform heterogeneous nucleation kinetics mediated by surface chemistry. Journal of the American Chemical Society, 141 (34), 13516-13524.

  4. Wang, M., Park, C., & Woehl, T. J. (2018) Quantifying the nucleation and growth kinetics of electron beam nanochemistry with liquid cell scanning transmission electron microscopy. Chemistry of Materials. 30 (21), pp 7727–7736.

  5. Touve, M. A., Figg, C. A., Wright, D. B., Park, C., Cantlon, J., Sumerlin, B. S. & Gianneschi, N. C. (2018). Polymerization-induced self-assembly of micelles observed by liquid cell transmission electron microscopy. ACS Central Science. 4 (5), pp 543–547.

  6. Moser, T., Mehta, H., Park, C., Kelly, R., Shokuhfar, T. & Evans, J.E. (2018). The role of electron irradiation history in liquid cell transmission electron microscopy. Science Advances. 4(4).

  7. Parent, L., Bakalis, E., Proetto, M., Li, Y., Park, C., Zerbetto, F., Gianneschi, N. (2018) Tackling the challenges of experimenting with liquid phase transmission electron microscopy. Accounts of Chemical Research. 51(1). 3-11.

  8. Parent, L., Bakalis, E., Ramirez-Hernandez, A., Kammeyer, J. K., Park, C., Pablo J. d., Zerbetto, F., Patterson, J. P., & Gianneschi, N. C. (2017). Directly observing micelle fusion and growth in solution by liquid-cell transmission electron microscopy. Journal of the American Chemical Society, 139(47), 17140–17151.

  9. Smith, B., Parent, L., Overholts, A., Beaucage, P., Bisbey, R., Chavez, A., Hwang, N., Park, C., Evans, A., Gianneschi, N., & Dichtel, W. (2017). Colloidal covalent organic frameworks. ACS Central Science. 3 (1), 58–65.

  10. Mehdi, B. L., Qian, J., Park, C., Stevens, A., Xu, W., Henderson, W. A., Zhang, J.-G., Mueller, K. T., & Browning, N. D. (2016). The impact of Li grain size on coulombic efficiency in Li batteries. Scientific Reports, 6, 34267.

  11. Welch, D. A., Woehl, T., Park, C., Faller, R., Evans, J. E., & Browning, N. D. (2016). Understanding the role of solvation forces on the preferential attachment of nanoparticles in liquid. ACS Nano, 10 (1), 181–187.

  12. Abellan, P., Parent, L., Al Hasan, N., Park, C., Arslan, I., Karim, A., Evans, J., & Browning, N. (2016). Gaining control over the radiolytic synthesis of uniform sub-3nm Palladium nanoparticles; the use of aromatic liquids in the electron microscope. Lagmuir. 32(6), 1468–1477.

  13. Patterson, J. P., Abellan-Baez, P., Denny, M. S., Jr., Park, C., Browning, N. D., Cohen, S. M., Evans, J. E., & Gianneschi, N. C. (2015). Liquid cell transmission electron microscopy of metal-organic-frameworks. Journal of American Chemical Society, 137(23), 7322-7328.

  14. Mehdi, B. L., Qian, J., Nasybulin, E., Park, C., Welch, D. A., Faller, R., Mehta, H., Henderson, W. A., Xu, W., Wang, C. M., Evans, J. E., Liu, J., Zhang, J. G., Mueller, K. T., & Browning, N. D. (2015). Observation and quantification of nanoscale processes in Lithium batteries by operando electrochemical (S)TEM. Nano Letters, 15(3), 2168–2173.

  15. Abellán, P., Mehdi, B. L., Parent, L. R., Gu, M., Park, C., Xu, W., Zhang, Y., Arslan, I., Zhang, J. G., & Wang, C. (2014). Probing the degradation mechanisms in electrolyte solutions for Li-ion batteries by in situ TEM. Nano Letters, 14(3), 1293–1299.

  16. Woehl, T. J., Park, C., Evans, J. E., Arslan, I., Ristenpart, W. D., & Browning, N. D. (2014). Direct observation of aggregative nanoparticle growth: kinetic modeling of the size distribution and growth rate. Nano Letters, 14(1), 373-378.

  17. Huitink, D., Kundu, S., Park, C., Mallick, B., Huang, J. Z., & Liang, H. (2010). Nanoparticle shape evolution identified through multivariate statistics. Journal of Physical Chemistry A, 114(17), 5596–5600.

Application (Manufacturing, Energy Systems)

  1. Park, C. Rao, R., Nikolaev, P. & Maruyama, B. (2022) Gaussian process surrogate modeling under control uncertainties for yield prediction of carbon nanotube production processes. ASME Transactions, Journal of Manufacturing Science and Engineering. 144(3), 1-10.

  2. Jeong, D., Park, C. & Ko, Y. (2021). Missing data imputation using mixture factor analysis for building electric load data. Applied Energy. 304(15), 117655.

  3. Park, C. and Ding, Y., (2021) Dynamic Data-Driven Monitoring of Nanoparticle Self Assembly Processes. In Handbook of Dynamic Data Driven Applications Systems – 2nd Ed. Springer. ISBN 978-3-030-74567-7.

  4. Jeong, D., Park, C. & Ko, Y. (2021) Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration. Applied Energy. 282 (B), 116249.

  5. C. Park, & Y. Ding (2020) Dynamic Data-Driven Distribution Tracking of Nanoparticle Morphology. Dynamic Data Driven Application Systems (pp. 132-139). Springer, Cham.

  6. Ren, J., Park, C. & Wang, H. (2018) Stochastic modeling and diagnosis of leak areas for surface assembly. ASME Transactions, Journal of Manufacturing Science and Engineering. 140(4): 1-10.

  7. Agbabiaka, A., & Park, C. (2016). SDP-based ensemble pruning algorithm with an improved re-sizing step. International Journal of Data Mining, Modelling and Management, 8(1), 12.

  8. Mishra, S., Vanli, A. O., & Park, C. (2015). A multivariate cumulative sum method for continuous damage monitoring with lamb-wave sensors. International Journal of Prognostics and Health Management, 6, 1-11.

  9. Park, C., Tang, J., & Ding, Y. (2010). Aggressive data reduction for damage detection in structural health monitoring. Structural Health Monitoring, 9(1), 59-74.