ImPhys Colloquium | Felix Hermann | June 29
26 mei 2021 13:00 t/m 14:00 | Zet in mijn agenda
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 (under joint supervision of Professor Berkhout and Wapenaar). After research positions at Stanford University and the Massachusetts Institute of Technology, he became in 2002 faculty at the University of British Columbia.
In 2017, he joined the Georgia Institute of technology where he is now 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 computational 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”.
For his contributions to seismic data acquisition with compressive sensing, he was given the 2020 SEG Reginald Fessenden Award. 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.
Research Area
- Geophysics
Research Interests
- Computational Seismic Imaging
- Inverse Problems
- Machine Learning
Felix Hermann
Learned wave-based imaging – variational inference at scale
High dimensionality, complex physics, and lack of access to the ground truth rank medical ultrasound and seismic imaging amongst the most challenging problems in the computational imaging sciences. If these challenges were not bad enough, modern applications of computational imaging increasingly call for the assessment of uncertainty on the image itself and on subsequent tasks. During this talk, I will show how recent developments in Normalizing Flows, a new type of invertible neural networks, can be used to cast wave-based imaging into a scalable Bayesian framework. Contrary to conventional methods, where sample images are drawn from the posterior distribution during inversion, our approach trains Normalizing Flows capable of generating samples from the posterior. Aside from greatly reducing the computational cost, this approach gives us access to the image itself (via Maximum a posteriori or mean estimation) and its multidimensional statistical distribution including its pointwise variance.
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