Publications

Preprints/Submitted:

• Park, J., Park, C., Ahn, J. (2025+) A geometry-preserving framework for sufficient dimension reduction of compositional data. arXiv:2509.05563. Python codes.
• Park, J., Kok, N., and Gaynanova, I. (2025+) Beyond fixed thresholds: optimizing summaries of wearable device data via piecewise linearization of quantile functions. arXiv:2501.11777.

Peer-reviewed articles:

• Park, J., Ahn, J., and Park, C. (2023) Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data, Proceedings of the 40th International Conference on Machine Learning (ICML). Github.
• Kang, I., Choi, H., Yoon, Y.-J., Park, J., Kwon, S.-S., and Park, C. (2023), Frechet Distance-Based Cluster Analysis for Multi-Dimensional Functional Data, Statistics and Computing, 33(4), 75.
• Park, J., Yoon, C., Park, C., and Ahn, J. (2022), Kernel Methods for Radial Transformed Compositional Data with Many Zeros, Proceedings of the 39th International Conference on Machine Learning (ICML).

In Progress (coming soon!)

• Frechet regression of multivariate distributions under the semiparametric nonparanormal model (with Irina Gaynanova)
• Fast distance computation of multivariate distributions (with Edward Shao, Irina Gaynanova)