Probability theory: limit theorems for Markov chains, large deviation theory, stochastic
processes and stochastic ODEs

Dynamical systems and ergodic theory

Statistics with applications to data science

Machine Learning interpretability

Publications

A. Miroshnikov, K. Kotsiopoulos, and A. Ravi Kannan, “Mutual information-based group explainers with coalition structure for machine learning model explanations”, (2021), arxiv:2102.10878.

A. Miroshnikov, K. Kotsiopoulos, R. Franks and A. Ravi Kannan, “Wasserstein-based fairness interpretability framework for machine learning models”, (2020), arxiv:2011.03156.

A. Miroshnikov, K. Kotsiopoulos and E. Conlon, “Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods“, Submitted (2018), arxiv:1710.09071.