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O
Yijun Zhang, Ziyi Yin, Oscar Lopez, Ali Siahkoohi, Mathias Louboutin, Rajiv Kumar, and Felix J. Herrmann, “Optimized time-lapse acquisition design via spectral gap ratio minimization”, Geophysics, vol. 88, pp. A19-A23, 2023.
N
Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “Neural network augmented wave-equation simulation”, Georgia Institute of Technology, TR-CSE-2019-1, 2019.
M
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, Philipp A. Witte, Mathias Louboutin, and Felix J. Herrmann, “Multifidelity conditional normalizing flows for physics-guided Bayesian inference”, ML4SEISMIC Partners Meeting. 2021.
Felix J. Herrmann, Mathias Louboutin, and Ali Siahkoohi, “ML@scale using randomized linear algebra”, in Microsoft, 2021.
L
Mathias Louboutin, Ali Siahkoohi, Rongrong Wang, and Felix J. Herrmann, “Low-memory stochastic backpropagation with multi-channel randomized trace estimation”, TR-CSE-2021-1, 2021.
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 and Felix J. Herrmann, “Learning by example: fast reliability-aware seismic imaging with normalizing flows”, in SEG Technical Program Expanded Abstracts, 2021, pp. 1580-1585.
Felix J. Herrmann, Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, Philipp A. Witte, and Mathias Louboutin, “Learned wave-based imaging - variational inference at scale”, in Delft, 2021.
Mathias Louboutin, Rafael Orozco, Ali Siahkoohi, and Felix J. Herrmann, “Learned non-linear simultenous source and corresponding supershot for seismic imaging.”, in International Meeting for Applied Geoscience and Energy, 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.
Gabrio Rizzuti, Ali Siahkoohi, and Felix J. Herrmann, “Learned iterative solvers for the Helmholtz equation”, in EAGE Annual Conference Proceedings, 2019.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, HotCSE Seminar. 2019.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, in Neural Information Processing Systems (NeurIPS), 2019.
Ziyi Yin, Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
J
Ziyi Yin, Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, Henryk Modzelewski, and Felix J. Herrmann, “Julia for Geoscience”, Transform. 2022.
I
Philipp A. Witte, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Bas Peters, and Felix J. Herrmann, “InvertibleNetworks.jl - Memory efficient deep learning in Julia”, in JuliaCon, 2021.
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.
Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “The importance of transfer learning in seismic modeling and imaging”, Geophysics, 2019.
F
Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, and Felix J. Herrmann, “Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows”, Georgia Institute of Technology, TR-CSE-2020-2, 2020.
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, Philipp A. Witte, Mathias Louboutin, and Felix J. Herrmann, “Fast and reliability-aware seismic imaging with conditional normalizing flows”, in Intelligent illumination of the Earth, 2021.
E
Rajiv Kumar, Maria Kotsi, Ali Siahkoohi, and Alison Malcolm, “Enabling uncertainty quantification for seismic data pre-processing using normalizing flows (NF)—an interpolation example”, in SEG Technical Program Expanded Abstracts, 2021, pp. 1515-1519.
D
Ali Siahkoohi, Rajiv Kumar, and Felix J. Herrmann, “Deep-learning based ocean bottom seismic wavefield recovery”, in SEG Technical Program Expanded Abstracts, 2019, pp. 2232-2237.
Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification”, GT SEG Student Chapter. 2020.
Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann, “A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification”, in EAGE Annual Conference Proceedings, 2020.
Ali Siahkoohi, “Deep generative models for solving geophysical inverse problems”, Georgia Institute of Technology, Atlanta, 2022.

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