My research interests include a variety of topics such as data science, computational statistics (especially causal inference), game theory and stochastic processes. Currently, I am focusing on the application of Markov chains to discrete and continuous systems from statistical physics and the resulting algorithmic applications.
A variety of connections between theoretical computer science and statistical physics has been investigated within recent years. Some of the most remarkable results relate phase transitions of physical systems with the traceability of computational problems. The ongoing effort to connect those scientific branches leads to a two-way exchange: tools from statistical physics are used to explain computational properties of various algorithmic problems, and results from theoretical computer science are used to investigate phenomena from statistical physics. Probabilistic properties, such as spatial decay of correlations and rapid mixing of certain Markov chains, seem to be at the very core of these connections.