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2021
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, and Felix J. Herrmann, “Preconditioned training of normalizing flows for variational inference in inverse problems”, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.
Gabrio Rizzuti, Mathias Louboutin, Rongrong Wang, and Felix J. Herrmann, “Time-domain Wavefield Reconstruction Inversion for large-scale seismic inversion”, in SIAM Conference on Mathematical and Computational Issues in the Geosciences, 2021.
Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach”, ML4SEISMIC Partners Meeting. 2021.
Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “Variational inference for artifact removal of adjoint solutions in photoacoustic problems”, ML4SEISMIC Partners Meeting. 2021.
2022
Mathias Louboutin, Ali Siahkoohi, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Yijun Zhang, Philipp A. Witte, Gabrio Rizzuti, and Felix J. Herrmann, “Abstractions for at-scale seismic inversion”, in Rice Oil and Gas High Performance Computing Conference 2022, 2022, p. Thursday Workshop: Devito Training and Hackathon.
Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi, Gabrio Rizzuti, Ziyi Yin, Rafael Orozco, and Felix J. Herrmann, “Accelerating innovation with software abstractions for scalable computational geophysics”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “Deep Bayesian inference for seismic imaging with tasks”, Geophysics, vol. 87, pp. 281-302, 2022.
Ziyi Yin, Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, Henryk Modzelewski, and Felix J. Herrmann, “Julia for Geoscience”, Transform. 2022.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, “Low-cost uncertainty quantification for large-scale inverse problems”, ML4SEISMIC Partners Meeting. 2022.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, “Reliable amortized variational inference with conditional normalizing flows via physics-based latent distribution correction”, in IMAGE Workshop on Subsurface Uncertainty Description and Estimation - Moving Away from Single Prediction with Distribution Learning, 2022.
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, and Felix J. Herrmann, “Wave-equation based inversion with amortized variational Bayesian inference”, in EAGE Annual Conference Proceedings, 2022, p. Session 2: Velocity model building and imaging (different domains).
2023
Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix J. Herrmann, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix J. Herrmann, “Amortized normalizing flows for transcranial ultrasound with uncertainty quantification”, in Medical Imaging with Deep Learning, 2023.
Rafael Orozco, Philipp A. Witte, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Bas Peters, and Felix J. Herrmann, “InvertibleNetworks.jl: A Julia package for scalable normalizing flows”. 2023.
Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman, and Felix J. Herrmann, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.

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