个人简介

Dr Matthias Sachs, Assistant Professor in Applied Mathematics and Statistics.

联系方式

邮件：m.sachs@bham.ac.uk

任职及教育经历

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

研究领域

Matthias Sachs is an applied mathematician working at the interface of numerical analysis, probability theory and statistical modelling. As part of his research, he tries to produce sound and rigorously derived mathematical theory while at the same time provide practically relevant algorithmic solutions and results. His 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 his time with software development to provide computationally efficient implementations of his and his collaborators' work to other researchers.

近期学术出版物

Article

Witt, WC, Oord, CVD, Gelžinytė, E, Järvinen, T, Ross, A, Darby, JP, Ho, CH, Baldwin, WJ, Sachs, M, Kermode, J, Bernstein, N, Csányi, G & Ortner, C 2023, 'ACEpotentials.jl: A Julia implementation of the atomic cluster expansion', The Journal of Chemical Physics, vol. 159, no. 16, 164101. https://doi.org/10.1063/5.0158783

van der Oord, C, Sachs, M, Kovács, DP, Ortner, C & Csányi, G 2023, 'Hyperactive learning for data-driven interatomic potentials', npj Computational Materials, vol. 9, no. 1, 168. https://doi.org/10.1038/s41524-023-01104-6

Sachs, M, Sen, D, Lu, J & Dunson, D 2023, 'Posterior computation with the Gibbs zig-zag sampler', Bayesian Analysis, vol. 18, no. 3, pp. 909-927. https://doi.org/10.1214/22-BA1319

Leimkuhler, B & Sachs, M 2022, 'Efficient numerical algorithms for the generalized Langevin equation', SIAM Journal on Scientific Computing, vol. 44, no. 1, pp. A364-A388. https://doi.org/10.1137/20M138497X

Sen, D, Sachs, M, Lu, J & Dunson, DB 2020, 'Efficient posterior sampling for high-dimensional imbalanced logistic regression', Biometrika, vol. 107, no. 4, pp. 1005-1012. https://doi.org/10.1093/biomet/asaa035

Leimkuhler, B, Sachs, M & Stoltz, G 2020, 'Hypocoercivity Properties of Adaptive Langevin Dynamics', SIAM Journal on Applied Mathematics, vol. 80, no. 3, pp. 1197-1222. https://doi.org/10.1137/19M1291649

Lu, J, Sachs, M & Steinerberger, S 2020, 'Quadrature Points via Heat Kernel Repulsion', Constructive Approximation, vol. 51, no. 1, pp. 27-48. https://doi.org/10.1007/s00365-019-09471-4

Sachs, M, Leimkuhler, B & Danos, V 2017, 'Langevin dynamics with variable coefficients and nonconservative forces: From stationary states to numerical methods', Entropy, vol. 19, no. 12, 647. https://doi.org/10.3390/e19120647

Conference contribution

Leimkuhler, B & Sachs, M 2019, Ergodic Properties of Quasi-Markovian Generalized Langevin Equations with Configuration Dependent Noise and Non-conservative Force. in G Giacomin, S Olla, E Saada, H Spohn, G Stoltz & G Stoltz (eds), Stochastic Dynamics Out of Equilibrium - Institut Henri Poincaré, 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. https://doi.org/10.1007/978-3-030-15096-9_8

Preprint

Witt, WC, Oord, CVD, Gelžinytė, E, Järvinen, T, Ross, A, Darby, JP, Ho, CH, Baldwin, WJ, Sachs, M, Kermode, J, Bernstein, N, Csányi, G & Ortner, C 2023 'ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion' arXiv. https://doi.org/10.48550/arXiv.2309.03161

Oord, CVD, Sachs, M, Kovács, DP, Ortner, C & Csányi, G 2022 'Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials'. https://doi.org/10.48550/arXiv.2210.04225

Herschlag, G, Mattingly, JC, Sachs, M & Wyse, E 2020 'Non-reversible Markov chain Monte Carlo for sampling of districting maps' arXiv. https://doi.org/10.48550/arXiv.2008.07843