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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, “Towards generative seismic kriging with normalizing flows”, ML4SEISMIC Partners Meeting. 2023.
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, “Improved automatic seismic CO2 leakage detection via dataset augmentation”, ML4SEISMIC Partners Meeting. 2023.
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, “Maximizing CO2 injectivity within fracture pressure”, ML4SEISMIC Partners Meeting. 2023.
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, “Solving PDE-based inverse problems with learned surrogates and constraints”, HotCSE Seminar. 2023.
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, “Large-scale parametric PDE approximations with model-parallel Fourier neural operators”, ML4SEISMIC Partners Meeting. 2023.
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, “WISE: Full-waveform Inference with Subsurface Extensions”, ML4SEISMIC Partners Meeting. 2023.
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, “Digital twins in the era of generative AI”, The Leading Edge, vol. 42, 2023.
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, “Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model”, ML4SEISMIC Partners Meeting. 2023.
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, “End-to-end permeability inversion from prestack time-lapse seismic data: a case study on Compass model”, ML4SEISMIC Partners Meeting. 2023.
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, “CO2 reservoir monitoring through Bayesian data assimilation”, ML4SEISMIC Partners Meeting. 2023.
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, “Uncertainty quantification so what? Leveraging probabilistic seismic inversion for experimental design”, ML4SEISMIC Partners Meeting. 2023.
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, “Monitoring subsurface CO2 plumes with learned sequential Bayesian inference”, ML4SEISMIC Partners Meeting. 2023.
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, “Solving multiphysics-based inverse problems with learned surrogates and constraints”, Advanced Modeling and Simulation in Engineering Sciences, vol. 10, 2023.
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, “Inference of CO2 flow patterns – a feasibility study”, in Neural Information Processing Systems (NeurIPS), 2023.
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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.”, 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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, “Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference”, in International Meeting for Applied Geoscience and Energy, 2023.
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, “Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model”, in International Meeting for Applied Geoscience and Energy, 2023.
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, “Coupled physics inversion for geological carbon storage monitoring”, in International Meeting for Applied Geoscience and Energy, 2023.