About me: I graduated from the University of Hamburg with a Master's degree in Geophysics in 2014 and joined SLIM in the following fall. My research interests include linear and nonlinear seismic inversion as well as anisotropic modelling.
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, “InvertibleNetworks.jl: A Julia package for scalable normalizing flows”, Journal of Open Source Software, vol. 9, 2024.
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, “Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs”, Computers & Geosciences, vol. 178, p. 105402, 2023.
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, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
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, “Effective scaling of numerical surrogates via domain-decomposed Fourier neural operators”, ML4SEISMIC Partners Meeting. 2022.
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, “Accelerating innovation with software abstractions for scalable computational geophysics”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
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, “Julia for Geoscience”, Transform. 2022.
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, “Abstractions for at-scale seismic inversion”, in Rice Oil and Gas High Performance Computing Conference 2022, 2022, p. Thursday Workshop: Devito Training and Hackathon.
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, “Seismic Velocity Inversion and Uncertainty Quantification Using Conditional Normalizing Flows”, in AGU Annual Meeting, 2021, p. U12A-03.
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, “Multifidelity conditional normalizing flows for physics-guided Bayesian inference”, ML4SEISMIC Partners Meeting. 2021.
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, “Redwood – towards clusterless supercomputing in the cloud”, ML4SEISMIC Partners Meeting. 2021.
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, “Low-cost time-lapse seismic imaging of CCS with the joint recovery model”, in SEG Workshop on Geophysical Challenges in Presalt Carbonates; virtual, 2021.
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, “Fast and reliability-aware seismic imaging with conditional normalizing flows”, in Intelligent illumination of the Earth, 2021.
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, “InvertibleNetworks.jl - Memory efficient deep learning in Julia”, in JuliaCon, 2021.
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, “Learned wave-based imaging - variational inference at scale”, in Delft, 2021.
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, “Deep Bayesian Inference for Task-based Seismic Imaging”, in KAUST, 2021.
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, “Preconditioned training of normalizing flows for variational inference in inverse problems”, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.
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, “Seismic Imaging with Uncertainty Quantification: Sampling from the Posterior with Generative Networks”, in SIAM Conference on Imaging Science, 2020.
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, “Scaling through abstractions – high-performance vectorial wave simulations for seismic inversion with Devito”, Georgia Institute of Technology, TR-CSE-2020-3, 2020.
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, “Extended source imaging –- a unifying framework for seismic and medical imaging”, in SEG Technical Program Expanded Abstracts, 2020, pp. 3502-3506.
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, “Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization”, in SEG Technical Program Expanded Abstracts, 2020, pp. 1541-1545.