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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, 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.
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.
Ziyi Yin, Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, and Felix J. Herrmann, “Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates”, in Engineering Mechanics Institute Conference, 2023.
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, “Deep generative models for solving geophysical inverse problems”, Georgia Institute of Technology, Atlanta, 2022.
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.
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, Chinen, M., Denton, T., W. Kleijn, B., and Skoglund, J., “Ultra-Low-Bitrate Speech Coding with Pretrained Transformers”, in Proceedings of INTERSPEECH, 2022.
Ziyi Yin, Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion”, ML4SEISMIC Partners Meeting. 2022.
2021
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.
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.
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, Mathias Louboutin, and Ali Siahkoohi, “ML@scale using randomized linear algebra”, in Microsoft, 2021.
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.
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, 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, 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, Gabrio Rizzuti, Mathias Louboutin, and Felix J. Herrmann, “Unsupervised data-guided uncertainty analysis in imaging and horizon tracking”, in SIAM Texas-Louisiana, 2020.
2019
Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “The importance of transfer learning in seismic modeling and imaging”, Geophysics, 2019.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, in Neural Information Processing Systems (NeurIPS), 2019.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, HotCSE Seminar. 2019.
Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “Neural network augmented wave-equation simulation”, Georgia Institute of Technology, TR-CSE-2019-1, 2019.