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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“Optimizing CO2 Storage Monitoring with Enhanced Rock Physics Modeling and Optimal Pressure Control”, in CCUS 2025 - Carbon Capture, Utilization, and Storage Conference, 2025. ,
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“WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction”, Geophysics, vol. 90, 2025. ,
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“Digital Twins in the era of generative AI — Application to Geological CO2 Storage”, in ICON Seminar in IoT, 2024. ,
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“Velocity Continuation for Common Image Gathers with Fourier Neural Operators”, in International Meeting for Applied Geoscience and Energy, 2024. ,
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“Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations”, in International Meeting for Applied Geoscience and Energy, 2024. ,
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“Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation”, in International Meeting for Applied Geoscience and Energy, 2024. ,
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“WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements”, in International Meeting for Applied Geoscience and Energy, 2024. ,
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“BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows - a case study in optimal monitor well placement for CO2 sequestration”, in International Meeting for Applied Geoscience and Energy, 2024. ,
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“Time-lapse full-waveform permeability inversion: a feasibility study”, The Leading Edge, vol. 43, 2024. ,
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“A Digital Twin for Geological Carbon Storage with Controlled Injectivity”, in International Meeting for Applied Geoscience and Energy, 2024. ,
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“DT4GCS — Digital Twin for Geological CO2 Storage and Control”, in Geophysical Research for Gigatonnes CO2 Storage, Colorado School of Mines, 2024. ,
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“DT4GCS –- Digital Twin for Geological CO2 Storage and Control”, in Gigatonnes CO2 Storage Workshop, 2024. ,
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“Coupled Permeability Inversion from Time-Lapse Seismic Data”, in Geophysical Research for Gigatonnes CO2 Storage, Colorado School of Mines, 2024. ,
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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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“Digital Twins in the era of generative AI - Application to Geological CO2 Storage”, ICL Seminar. 2024. ,
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“Neural wave-based imaging with amortized uncertainty quantification”, ICL Seminar. 2024. ,
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“Normalizing Flows for Bayesian Experimental Design in Imaging Applications”, in EAGE Annual Conference Proceedings, 2024. ,
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“Neural wave-based imaging with amortized uncertainty quantification”, in Inverse Problems: Modelling and Simulation, 2024. ,