My current research interest is in statistical aspects of machine learning.
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.
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.
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.
Khim, J., Xu, Z., and Singh, S. 2020. Multiclass Classification via Class-Weighted Nearest Neighbors. 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. and Loh, P. 2018. Adversarial Risk Bounds for Binary Classification via Function Transformation. Arxiv.
Khim, J. and Loh, P. 2018. A theory of maximum likelihood for weighted infection graphs. Arxiv.
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.