Research

My PhD research was in network science, meaning problems inspired by social, biological, computer, and other types of networks. Network science draws on contributions from researchers in many fields, such as economics, computer science, physics, applied math, engineering, probability, and statistics. As an example, I studied whether a network explains the propagation of a particular process and how to construct networks that explain processes from data.

During my postdoctoral fellowship, I worked on statistical aspects of machine learning. Some concrete problems included imbalanced classification, adversarial examples, and other variations on statistical learning. I also worked on applied problems in statistical climate prediction and natural language processing.

CV/Resume

Papers

Singh, S. and Khim, J. 2022. Optimal binary classification beyond accuracy. Advances in Neural Information Processing Systems.

Khim, J. and Loh, P. 2021. Permutation tests for infection graphs. Journal of the American Statistical Association 116 (534), 770-782.

Khim, J., Leqi, L., Prasad, A., and Ravikumar, P. 2020. Uniform Convergence of Rank-Weighted Learning. Proceedings of the 37th International Conference on Machine Learning.

Bali, S., Zheng, S., Gupta, A., Wu, Y., and Chen, B., Chowdhury, A., and Khim, J. 2021. Prediction of boreal peatland fires in Canada using spatio-temporal methods. Climate Change AI, ICML.

Xu, Z., Dan, C., Khim, J., and Ravikumar, P. 2020. Class-Weighted Classification: Trade-offs and Robust Approaches. Proceedings of the 37th International Conference on Machine Learning. Arxiv.

Kim, J., Gong, L., Khim, J., Weiss, J., and Ravikumar, P. 2020. Improved clinical abbreviation expansion via non-sense-based approaches. Machine Learning for Health Workshop (ML4H).

Khim, J., Jog, V., and Loh, P. 2019. Adversarial Influence Maximization. IEEE International Symposium on Information Theory. Arxiv. Conference Paper.

Khim, J. and Loh, P. 2018. Adversarial Risk Bounds for Binary Classification via Function Transformation. Arxiv.

Khim, J. and Loh, P. 2017. Confidence sets for the source of a diffusion in regular trees. IEEE Transactions on Network Science and Engineering. Volume 4, Issue 1. Arxiv. Journal.

Khim, J., Jog, V., and Loh, P. 2016. Computing and maximizing in linear threshold and triggering models. Advances in Neural Information Processing Systems. Conference Paper.