Research Area: My research interests include machine learning, signal processing, numerical methods, and inverse problems. Currently, my research is mainly focused on applications of deep learning in computational inverse problems.
Homepage: https://alisiahkoohi.github.io/
About me: I am currently pursuing a Ph.D. in Computational Science and Engineering under the supervision of Dr. Felix J. Herrmann. I completed my B.Sc. in Electrical Engineering at Sharif University of Technology and my M.Sc. in Geophysics at University of Tehran.
Homepage: https://alisiahkoohi.github.io/
About me: I am currently pursuing a Ph.D. in Computational Science and Engineering under the supervision of Dr. Felix J. Herrmann. I completed my B.Sc. in Electrical Engineering at Sharif University of Technology and my M.Sc. in Geophysics at University of Tehran.
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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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, “Neural wave-based imaging with amortized uncertainty quantification”, ICL Seminar. 2024.
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, “Neural wave-based imaging with amortized uncertainty quantification”, in Inverse Problems: Modelling and Simulation, 2024.
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, “Learned non-linear simultenous source and corresponding supershot for seismic imaging.”, in International Meeting for Applied Geoscience and Energy, 2023.
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, “3D seismic survey design by maximizing the spectral gap”, in International Meeting for Applied Geoscience and Energy, 2023.
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, “Optimized time-lapse acquisition design via spectral gap ratio minimization”, Geophysics, vol. 88, pp. A19-A23, 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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, “Amortized normalizing flows for transcranial ultrasound with uncertainty quantification”, in Medical Imaging with Deep Learning, 2023.
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, “Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates”, in Engineering Mechanics Institute Conference, 2023.
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, “Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics”, in 5th Symposium on Advances in Approximate Bayesian Inference, 2023.
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, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.
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, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
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, “Time-lapse seismic survey design by maximizing the spectral gap”, ML4SEISMIC Partners Meeting. 2022.
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, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
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, “Low-cost uncertainty quantification for large-scale inverse problems”, ML4SEISMIC Partners Meeting. 2022.
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, “Amortized velocity continuation with Fourier neural operators”, ML4SEISMIC Partners Meeting. 2022.
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, “Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion”, ML4SEISMIC Partners Meeting. 2022.
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, “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.
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, “Deep Bayesian inference for seismic imaging with tasks”, Geophysics, vol. 87, pp. 281-302, 2022.