Benjamin Kurt Miller
PhD student at AMLab and GRAPPA in the AI4Science initiative. The goal is to understand, characterize, and generate gravitational waveforms using machine learning. This requires the development of new tools including likelihood-free inference and improved data-driven simulators. Generally, my focus is on applying deep learning to physics problems.
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.
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.
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. 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. (Delayed due to COVID-19)
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, 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 K. Miller. Proper Orthogonal Decomposition (Download pdf). Coursework at Freie Universitaet Berlin. 14 November 2018. Berlin, Germany.
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. Classical Analysis of High Harmonic Generation. University of Colorado Boulder Honors Thesis. 5 November 2015. Boulder, Colorado, USA.
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 Hönes - University of Amsterdam MSc in Artificial Intelligence
Mathematics Education Resources
I do not contribute to these sources but I am a fan. For that reason, I will share them here.