Research Themes
- Numerical analysis of stochastic differential equations;
- Computational statistics: design and analysis of Markov Chain Monte Carlo methods;
- Machine learning methods for molecular systems/dynamics: learnable equivariant representations of scalar/vector/tensor valued quantities, active learning.
Research Activity
Machine Learning for Molecular systems/dynamics
I work on the development and implementation of machine learning methods in the context of molecular modeling. This includes in particular
- equivariant representations of physical quantities such as inter-molecular forcefields and friction tensors that allow for data-efficient learning of such quantities;
- Bayesian inference methods based on the above mentioned equivariant representations;
- Active learning approaches for automatic data-generation of atomic configurations.
Design and analysis of sampling algorithms
I am interested in the design and analysis of sampling algorithms. Besides classical Markov Chain Monte Carlo algorithms this includes approximate Monte Carlo algorithms that are obtained as a discretization of stochastic differential equation. Among others I have been and am working on
- non-reversible Markov Chain Monte Carlo method for sampling of discrete probability measures (e.g., graph partitions),
- Stochastic Thermostat methods (e.g., Generalized/Adaptive Langevin dynamics) and Piecewise deterministic Markov processes for efficient sampling of Bayesian posterior distributions in the presence of big data.