Professor, team leader
+1 (404) 385-7069
CODA building 13th floor
Research Area: Computational Imaging; Machine Learning; PDE-constrained Optimization; Uncertainty Quantification; Randomized Linear Algebra
About me: Felix J. Herrmann graduated from Delft University of Technology in 1992 and received his Ph.D. in engineering physics from that same institution in 1997. After research positions at Stanford University and the Massachusetts Institute of Technology, he became in 2020 faculty at the University of British Columbia. In 2017, he joined the Georgia Institute of technology where he is a Georgia Research Alliance Scholar Chair in Energy, cross-appointed between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering. His cross-disciplinary research program spans several areas of computational imaging including seismic, and more recently, medical imaging. Dr. Herrmann is widely known for tackling challenging problems in the imaging sciences by adapting techniques from randomized linear algebra, PDE-constrained and convex optimization, high-performance computing, machine learning, and uncertainty quantification. Over his career, he has been responsible for several cost-saving innovations in industrial time-lapse seismic data acquisition and wave-equation based imaging. In 2019, he toured the world presenting the SEG Distinguished Lecture "Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition". In 2020, he was the recipient of the SEG Reginald Fessenden Award for his contributions to seismic data acquisition with compressive sensing. At Georgia Tech, he leads the Seismic Laboratory for Imaging and modeling and he is co-founder/director of the Center for Machine Learning for Seismic (ML4Seismic), designed to foster industrial research partnerships to drive innovations in artificial-intelligence assisted seismic imaging, interpretation, analysis, and time-lapse monitoring.
About me: Felix J. Herrmann graduated from Delft University of Technology in 1992 and received his Ph.D. in engineering physics from that same institution in 1997. After research positions at Stanford University and the Massachusetts Institute of Technology, he became in 2020 faculty at the University of British Columbia. In 2017, he joined the Georgia Institute of technology where he is a Georgia Research Alliance Scholar Chair in Energy, cross-appointed between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering. His cross-disciplinary research program spans several areas of computational imaging including seismic, and more recently, medical imaging. Dr. Herrmann is widely known for tackling challenging problems in the imaging sciences by adapting techniques from randomized linear algebra, PDE-constrained and convex optimization, high-performance computing, machine learning, and uncertainty quantification. Over his career, he has been responsible for several cost-saving innovations in industrial time-lapse seismic data acquisition and wave-equation based imaging. In 2019, he toured the world presenting the SEG Distinguished Lecture "Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition". In 2020, he was the recipient of the SEG Reginald Fessenden Award for his contributions to seismic data acquisition with compressive sensing. At Georgia Tech, he leads the Seismic Laboratory for Imaging and modeling and he is co-founder/director of the Center for Machine Learning for Seismic (ML4Seismic), designed to foster industrial research partnerships to drive innovations in artificial-intelligence assisted seismic imaging, interpretation, analysis, and time-lapse monitoring.
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, “Spectral Gap-Based Seismic Survey Design”, IEEE Transactions on Geoscience and Remote Sensing, 2023.
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, “Learned non-linear simultenous source and corresponding supershot for seismic imaging.”, presented at the 08, 2023.
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, “Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference”, presented at the 08, 2023.
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, “Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model”, presented at the 08, 2023.
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, “Coupled physics inversion for geological carbon storage monitoring”, presented at the 08, 2023.
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, “Amortized Bayesian Full Waveform Inversion and Experimental Design with Normalizing Flows”, presented at the 08, 2023.
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, “Generative Seismic Kriging with Normalizing Flows”, presented at the 08, 2023.
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, “Optimized time-lapse acquisition design via spectral gap ratio minimization”, Geophysics, vol. 88, 2023.
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, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, 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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, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.
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, “Derisking geological storage with simulation-based seismic monitoring design and machine learning”, in Carbon, Capture, Utilization, and Storage, 2023.
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, “3D seismic survey design by maximizing the spectral gap”, TR-CSE-2023-1, 2023.
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, “Act normal that's crazy enough — an overview of seismic inversion with normalizing flows and surrogate modeling”, Scientific Computing, Applied and Industrial Mathematics (SCAIM) Seminar. 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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, “The Next Step: Interoperable Domain-Specific Programming”, in SIAM Conference on Computational Science and Engineering, 2023.
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, “Derisking geological carbon storage from high-resolution time-lapse seismic to explainable leakage detection”, The Leading Edge, vol. 42, pp. 69–76, 2023.