Dr Matthias Sachs PhD

Dr Matthias Sachs

School of Mathematics
Assistant Professor in Applied Mathematics and Statistics

Contact details

School of Mathematics
Watson Building
University of Birmingham
B15 2TT

I am an applied mathematician working at the interface of Numerical Analysis, Probability theory and Statistical modelling. As part of my research, I try to produce sound and rigorously derived mathematical theory whilst at the same time provide practically relevant algorithmic solutions and results. My approach to bridging this gap is to work in close collaborations with researchers in other areas of science (e.g., material science, applied statistics) and industry as well as spending a significant amount of my time with software development to provide computationally efficient implementations of my and my collaborators' work to other researchers.

Personal webpage


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


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


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.