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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“Digital Twins in the Era of Generative AI: Application to Geological CO 2 Storage”, HCMF Seminar. 2024. ,
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“Generative AI for full-waveform variational inference”, Georgia Tech Geophysics Seminar. 2024. ,
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“WISE: full-Waveform variational Inference via Subsurface Extensions”, Geophysics, 2024. ,
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“An Uncertainty-Aware Digital Twin for Geological Carbon Storage”, in SIAM Conference on Uncertainty Quantification, 2024. ,
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“Normalizing Flows for Bayesian Experimental Design in Imaging Applications”, in SIAM Conference on Uncertainty Quantification, 2024. ,
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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. ,