Benjamin Kurt Miller
My focus is on developing new probabilistic modeling and deep learning algorithms for physics and science. Right now, I’m interested in simulation-based inference / likelihood-free inference / implicit likelihood inference which is a method for solving inverse problems. Applications include gravitational waves, exoplanets, fast ratio bursts, multi-messenger astrophysics, the cosmic microwave background, and dark matter searches. I also have expertise in atomistic simulation and machine learning for molecules and materials.
Interning at Meta from August 2023 until January 2024. Recently interned with Microsoft Research Amsterdam from August 2022 until October 2022. Previously interned at Berkeley Lab from August 2019 until October 2019.
Currently a PhD student at AMLab and GRAPPA in the AI4Science initiative. Advised by Max Welling, Samaya Nissanke, Christoph Weniger, and Patrick Forré. I am also an ELLIS PhD student working with Max Welling and Gilles Louppe.
I was the primary developer of a simulation-based inference library called swyft, now maintained by Christoph Weniger. I’ve contributed algorithms to simulation-based inference libraries lampe and sbi.
Benjamin Kurt Miller, Marco Federici Christoph Weniger Patrick Forré. Simulation-based Inference with the Generalized Kullback-Leibler Divergence. ICML 2023 workshop on Synergy of Scientific and Machine Learning Modeling. 28 July 2023.
Arnaud Delaunoy*, Benjamin Kurt Miller*, Patrick Forré, Christoph Weniger, Gilles Louppe. Balancing Simulation-based Inference for Conservative Posteriors. 5th Symposium on Advances in Approximate Bayesian Inference. arXiv:2210.06170. 18 July 2023.
Uddipta Bhardwaj, James Alvey, Benjamin Kurt Miller, Samaya Nissanke, Christoph Weniger. Peregrine: Sequential simulation-based inference for gravitational wave signals. arXiv:2304.02035. 04 April 2023.
Benjamin Kurt Miller, Christoph Weniger, and Patrick Forré. Contrastive Neural Ratio Estimation. NeurIPS 2022. arXiv:2210.06170. 27 November 2022.
Benjamin Kurt Miller, Alex Cole, Christoph Weniger, Francesco Nattino, Ou Ku, Meiert W Grootes. swyft: Truncated Marginal Neural Ratio Estimation in Python. Journal of Open Source Software. 19 July 2022.
Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli. Generative coarse-graining of molecular conformations. International Conference on Machine Learning 2022. 17 July 2022.
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, 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.
* denotes equal contribution.
An overview of Simulation-based Inference. AI4Science Symposium. 24 Feb 2023. Amsterdam, the Netherlands.
An overview of Simulation-based Inference methods and software. Simulation-based inference with swyft. Jan 23rd - 26th 2023. Amsterdam, the Netherlands.
Truncated Marginal Neural Ratio Estimation with swyft. 5th Inter-experiment Machine Learning Workshop. May 9th - 13th 2022. CERN, Meyrin, Switzerland.
Truncated Marginal Neural Ratio Estimation with swyft. Likelihood-free in Paris. 21 April 2022. École Normale Supérieure, Paris, France.
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.
Constrained marginal likelihood-to-evidence ratio estimation. AI4Science Colloquium. 18 March 2021. Amsterdam, the Netherlands.
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.
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.
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.
Wenkai Pan - University of Amsterdam MSc in Artificial Intelligence
Nienke Duetz - University of Amsterdam MSc in Artificial Intelligence
Christoph Hönes - University of Amsterdam MSc in Artificial Intelligence
ICLR Workshop Physics4ML 2023
NeurIPS Machine Learning for Molecules Workshop 2021
NeurIPS Workshop on Machine Learning and the Physical Sciences 2021
Gilles Louppe at University of Liège in Belgium. Feb 13 - 17.
Gilles Louppe at University of Liège in Belgium. Jan 16 - 20.
Mathematics Education Resources
I do not contribute to these sources but I am a fan. For that reason, I will share them here.