麻豆精选

• PhD in Applied and Computational Mathematics, University of Edinburgh, 2017

• Since Dec 2021 Assistant Professor, University 麻豆精选, UK.
• 2020-2021 Postdoctoral Fellow, University of British Columbia, Vancouver, Canada.
• 2017-2020 SAMSI-postdoctoral associate, Duke University, NC, USA.

Semester 2

LC Data, Probability and Statistics (Jinan)

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 modelling. 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 discretisation of stochastic differential equation. Among others I have been and am working on are

• non-reversible Markov Chain Monte Carlo method for sampling of discrete probability measures (e.g., graph partitions),
• stochastic thermostat methods (e.g., generalised/adaptive Langevin dynamics) and piecewise deterministic Markov processes for efficient sampling of Bayesian posterior distributions in the presence of big data.