I am a fourth-year Ph.D. student in the Amsterdam Machine Learning Lab (AMLab) and the AI4Science lab of the University of Amsterdam. I work on developing new machine learning algorithms that can help solve scientific research questions. My current interests are scientific machine learning, physics-inspired machine learning, inverse problems, generative models and self-supervised inference.

David Ruhe

David Ruhe

Ph.D. Student at the University of Amsterdam


Posts


Selected Publications

Clifford Group Equivariant Neural Networks (NeurIPS 2023 Oral)

Clifford Group Equivariant Neural Networks (NeurIPS 2023 Oral)

$\mathrm{E}(n)$ steerable equivariance using Clifford's geometric algebra.

David Ruhe, Johannes Brandstetter, Patrick Forré

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Geometric Clifford Algebra Networks (ICML 2023)

Geometric Clifford Algebra Networks (ICML 2023)

Incorporating geometry into neural network transformations.

David Ruhe, Jayesh K. Gupta, Steven de Keninck, Max Welling, Johannes Brandstetter

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Self-Supervised Inference in State-Space Models (ICLR 2022)

Self-Supervised Inference in State-Space Models (ICLR 2022)

Learning Kalman filters with physics-informed models.

David Ruhe, Patrick Forré

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Normalizing Flows for Hierarchical Bayesian Analysis (ML4PHYS 2022)

Normalizing Flows for Hierarchical Bayesian Analysis (ML4PHYS 2022)

Inferring gravitational wave parameters using normalizing flows.

David Ruhe, Kaze Wong, Miles Cranmer, Patrick Forré

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Detecting Dispersed Radio Transients Using Convolutional Neural Networks (ASCOM)

Detecting Dispersed Radio Transients Using Convolutional Neural Networks (ASCOM)

Using neural networks to find high-energy events in the deep radio universe.

David Ruhe, Mark Kuiack, Antonia Rowlinson, Ralph Wijers, Patrick Forré

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