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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, “Learned Multiphysics Inversion with Differentiable Programming & Machine Learning: An open-source path from wave physics to CO₂ digital twins”, in EAGE Workshop: Open for Energy: Open Source, Open Data, Open Models, EAGE, Aberdeen, 2026.
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, “Foundation models for Full-Waveform Inference w/ Uncertainty Quatification”, in 1st AIHPG - Artificial Intelligence & High-Performance Geophysics Workshop, Natal, Brazil, 2026.
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, “Context- and uncertainty-aware Digital Twin for the Optimization of Underground Storage Operations”, in SIAM Geosciences Webinar Series, 2026.
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, “Uncertainty-aware Digital Twins for Monitoring and Optimizing of Underground Energy Operations”, in Landmark Innovation Forum & Expo 2026, LIFE2026, Orlando, FL, 2026.
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, “Uncertainty-aware Digital Twins for Monitoring and Optimizing of Geological Carbon Storage”, in Net-Zero Emissions: Critical Minerals, Hydrogen, Nuclear, Geothermal, Wind, CCUS, and AI, SEG Workshop, 2026.
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, “Decision Making and Control with Digital Twins for Underground Energy Storage”, in SIAM Conference on Uncertainty Quantification (UQ26), Minneapolis, MN, 2026.
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, “Amortized Full-Waveform Inference w/ Learned Summary Statistics”, FWI Beyond Structure Interpretation — Benefits and Challenges, SEG workshop. 2026.
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, “Context-aware Digital Twin for Underground Storage”, in Artificial Intelligence and Digital Twins for Earth Systems 2025, Austin, TX, 2025.
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, “Towards foundation models for subsurface priors and posteriors”, in Workshop on Scientific Machine Learning 2025, Austin, TX, 2025.
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, “Power-scaled Bayesian inference with score-based generative models”, in International Meeting for Applied Geoscience and Energy, 2025.
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, “Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring”, in International Meeting for Applied Geoscience and Energy, 2025.
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, “Efficient and scalable posterior surrogate for seismic inversion via wavelet score-based generative models”, in International Meeting for Applied Geoscience and Energy, 2025.