Broadly, my research is 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.

My current research focuses on statistical aspects of network science. In particular, I study whether a network explains the propagation of a particular process and how to construct networks that explain processes from data. I also enjoy aspects of network science related to machine learning.


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

Khim, J. and Loh, P. 2018. A theory of maximum likelihood for weighted infection graphs. Submitted. Arxiv.

Khim, J. and Loh, P. 2017. Permutation Tests for Infection Graphs. Submitted. 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.

Khim, J., Jog, V., and Loh, P. 2016. Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. Submitted. Arxiv.