Dr Matthias Sachs PhD

Dr Matthias Sachs

School of Mathematics
Assistant Professor in Applied Mathematics and Statistics

Contact details

Email
m.sachs@bham.ac.uk
Address
School of Mathematics
Watson Building
University 麻豆精选
Edgbaston
Birmingham
B15 2TT
UK

Matthias Sachs is an applied mathematician with a background in Numerical Analysis, Probability Theory, and Statistical Modeling. His research focuses on machine learning methods for Molecular Modeling and Materials Science and the analysis and design of sampling methods for (Bayesian) statistical computing applications and molecular simulations.

Matthias aims to produce sound and rigorously derived mathematical theory as well as practically relevant algorithmic solutions and results. He works closely with researchers in other areas of science (e.g., materials science and applied statistics) and has had successful collaborations with industry partners. As part of his work, he spends significant time on software development.

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Qualifications

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

Biography

  • 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.

Teaching

Semester 2

LC Data, Probability and Statistics (Jinan)

Research

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.

Publications

Recent publications

Article

Sachs, M, Stark, WG, Maurer, RJ & Ortner, C 2025, '', Machine Learning: Science and Technology, vol. 6, no. 1, 015016.

Witt, WC, Oord, CVD, Gel啪inyt臈, E, J盲rvinen, T, Ross, A, Darby, JP, Ho, CH, Baldwin, WJ, Sachs, M, Kermode, J, Bernstein, N, Cs谩nyi, G & Ortner, C 2023, '', The Journal of Chemical Physics, vol. 159, no. 16, 164101.

van der Oord, C, Sachs, M, Kov谩cs, DP, Ortner, C & Cs谩nyi, G 2023, '', npj Computational Materials, vol. 9, no. 1, 168.

Sachs, M, Sen, D, Lu, J & Dunson, D 2023, '', Bayesian Analysis, vol. 18, no. 3, pp. 909-927.

Leimkuhler, B & Sachs, M 2022, '', SIAM Journal on Scientific Computing, vol. 44, no. 1, pp. A364-A388.

Sen, D, Sachs, M, Lu, J & Dunson, DB 2020, '', Biometrika, vol. 107, no. 4, pp. 1005-1012.

Leimkuhler, B, Sachs, M & Stoltz, G 2020, '', SIAM Journal on Applied Mathematics, vol. 80, no. 3, pp. 1197-1222.

Lu, J, Sachs, M & Steinerberger, S 2020, '', Constructive Approximation, vol. 51, no. 1, pp. 27-48.

Sachs, M, Leimkuhler, B & Danos, V 2017, '', Entropy, vol. 19, no. 12, 647.

Conference contribution

Leimkuhler, B & Sachs, M 2019, . in G Giacomin, S Olla, E Saada, H Spohn, G Stoltz & G Stoltz (eds), Stochastic Dynamics Out of Equilibrium - Institut Henri Poincar茅, 2017. Springer Proceedings in Mathematics and Statistics, vol. 282, Springer, pp. 282-330, International workshop on Stochastic Dynamics out of Equilibrium, IHPStochDyn 2017, Paris, France, 12/06/17.

Preprint

Sachs, M, Stark, WG, Maurer, RJ & Ortner, C 2024 '' arXiv.

Witt, WC, Oord, CVD, Gel啪inyt臈, E, J盲rvinen, T, Ross, A, Darby, JP, Ho, CH, Baldwin, WJ, Sachs, M, Kermode, J, Bernstein, N, Cs谩nyi, G & Ortner, C 2023 '' arXiv.

Oord, CVD, Sachs, M, Kov谩cs, DP, Ortner, C & Cs谩nyi, G 2022 ''.

Herschlag, G, Mattingly, JC, Sachs, M & Wyse, E 2020 '' arXiv.