Exploring math and the world.

Benjamin Kurt Miller


PhD student at AMLab and GRAPPA in the AI4Science initiative. The goal of my research is to advance simulation-based inference (aka likelihood-free inference) so it becomes practically useful for physicists such as on inference tasks related to gravitational waveforms, multi-messenger astrophysics, the cosmic microwave background, and dark matter searches. This requires the development of new tools in implicit modeling and improved data-driven simulators. Generally, my focus is on applying deep learning to physics problems.

Advised by Max Welling, Samaya Nissanke, Christoph Weniger, and Patrick Forré. I am the primary developer of a simulation-based inference library called swyft.

Member of the Atomic Architects. I contribute to e3nn as a developer and used the E(3) equivariant neural network for regression on small organic molecules.

Former member for Artificial Intelligence for the Sciences at the Free University of Berlin. 2019 summer intern at Berkeley Lab. Former member of AG Nichtlineare Laserdynamik and Ultrafast AMO Theory. For some time, I worked as a data scientist at a financial tech company.

Here are my relevant social media links: GitHub LinkedIn Twitter and Google Scholar.


Christoph Hönes, Benjamin Kurt Miller, Ana M. Heras, Bernard H. Foing. Automatically detecting anomalous exoplanet transits. NeurIPS 2021 workshop on Machine Learning and the Physical Sciences. 13 Dec 2021.

Alex Cole, Benjamin Kurt Miller, et. al. Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation. arXiv: 2111.08030. 16 November 2021.

Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger. Truncated Marginal Neural Ratio Estimation. NeurIPS 2021. arXiv:2107.01214. 02 July 2021.

Benjamin Kurt Miller, Alex Cole, Gilles Louppe, and Christoph Weniger. Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time. NeurIPS workshop on Machine Learning and the Physical Sciences. arXiv:2011.13951. 11 Dec 2020.

Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, and Frank Noe. Relevance of rotationally equivariant convolutions for predicting molecular properties. NeurIPS workshop on Interpretable Inductive Biases and Physically Structured Learning. arXiv:2008.08461. 12 Dec 2020. (and Machine Learning for Molecules Workshop 2020)

Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller. Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. arXiv: 2007.02005. 7 July 2020.

Benjamin Kurt Miller. SE(3) Equivariant Neural Networks for Regression on Molecular Properties: The QM9 Benchmark. Refubium Freie Universität Berlin Repository. 31 March 2020. Berlin, Germany.

Benjamin K. Miller. Proper Orthogonal Decomposition (Download pdf). Coursework at Freie Universitaet Berlin. 14 November 2018. Berlin, Germany.

Benjamin K. Miller. Classical Analysis of High Harmonic Generation. University of Colorado Boulder Honors Thesis. 5 November 2015. Boulder, Colorado, USA.


Benjamin Kurt Miller. Truncated Marginal Neural Ratio Estimation. NeurIPS 2021 Ghent Meetup. 8 Dec 2021. Ghent, Belgium. (Canceled due to COVID-19)

Benjamin Kurt Miller, Patrick Forré, Christoph Weniger. Marginal Posteriors with Truncated Marginal Ratio Estimation. SIAM AN21. CP17 Probability and Statistics. 23 July 2021. Virtual.

Benjamin Kurt Miller. Constrained marginal likelihood-to-evidence ratio estimation. AI4Science Colloquium. 18 March 2021. Amsterdam, the Netherlands.

Benjamin Kurt Miller. Constrained Marginal Likelihood-to-evidence ratio estimation: projections of the posterior distribution. Atlas Experiment at CERN Statistics and Machine Learning Colloquium. 18 March 2021. Online.

Benjamin Kurt Miller. Simulation-based Marginal Inference. EuroMoonMars, Earth, Space & Innovation EMMESI Workshop. 16-19 March 2021. Virtual Hybrid Workshop.

Benjamin Kurt Miller, Alex Cole, Gilles Louppe, and Christoph Weniger. Simulation-efficient marginal posterior estimation with swyft. Gravitational waves: a new messenger to explore the universe. 1 March 2021. Digital Conference.

Benjamin Kurt Miller. Determining astrophysical parameters with machine learning. AI4Science Kickoff Workshop. 9 July 2020. Amsterdam, the Netherlands.

Benjamin K. Miller, Tess E. Smidt, Mario Geiger. Consequences of Symmetry on Articulating Machine Learning Tasks. Society for Industrial and Applied Mathematics Conference on Mathematics of Data Science. 5-7 May 2020. Cincinnati, Ohio, USA. (Canceled due to COVID-19)

Benjamin K. Miller, Tess E. Smidt, James A. Sethian. Applications of SE(3) Equivariant Neural Networks. Berkeley Lab Computing Science Summer Program Poster Session. 1 August 2019. Berkeley, California, USA.

Benjamin Kurt Miller, Christoph Redlich, Lina Jaurigue, Benjamin Lingnau, and Kathy Lüdge. Gain compression induced polarization mode competition in quantum-dot micropillar lasers: Effects of coherent feedback on multi-mode rate equations. Deutsche Physikalische Gesellschaft. 20 March 2017. Dresden, Germany.

Benjamin K. Miller and Agnieszka Jaron-Becker. Classically Modeling High Harmonic Generation with a focus on Elliptical Polarization. JILA Posterfest. 2 October 2015. Boulder, Colorado, USA.


Christoph Weniger, Benjamin Kurt Miller, Francesco Nattino, Alex Cole, Ou Ku, Meiert Willem Grootes, & Adam Coogan. (2021). undark-lab/swyft. Zenodo. https://doi.org/10.5281/zenodo.5752734.

Mario Geiger, Tess Smidt, Alby M., Benjamin Kurt Miller, et. al. (2021). e3nn/e3nn. Zenodo. https://doi.org/10.5281/zenodo.3724963.


Christoph Hönes - University of Amsterdam MSc in Artificial Intelligence


NeurIPS Workshop on Machine Learning and the Physical Sciences 2021
NeurIPS Machine Learning for Molecules Workshop 2021

Mathematics Education Resources

I do not contribute to these sources but I am a fan. For that reason, I will share them here.

Richard E. Borcherds
Tensors for Beginners by eigenchris
Math Doctor Bob
MIT OpenCourseWare Probabilistic Systems Analysis and Applied Probability
The Bright Side of Mathematics