Wednesday Nov 6, 12pm-1pm, CODA 1315
Learned imaging with constraints and uncertainty quantification
Advisor: Prof. Felix Herrmann
We outline new approaches to incorporate ideas from convolutional networks into wave-based least-squares imaging. The aim is to combine hand-crafted constraints with deep convolutional networks allowing us to directly train a network capable of generating samples from the posterior. The main contributions include combination of weak deep priors with hard handcrafted constraints and a possible new way to sample the posterior.
Ali Siahkoohi is currently pursuing a Ph.D. in Computational Science and Engineering under the supervision of Dr. Felix J. Herrmann. He completed his B.Sc. in Electrical Engineering and his M.Sc. in Geophysics. Currently, Ali's research is mainly focused on applications of deep learning in computational inverse problems.