Emanuel Sommer
PhD researcher in David Rügamer's lab working on Bayesian neural networks with emphasis on practical sampling, JAX tooling, and hybrid optimization–sampling approaches for scalable uncertainty quantification.
Best podcasts with Emanuel Sommer
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Jan 28, 2026 • 1h 20min
#150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik
David Rügamer, LMU professor working on uncertainty in deep models; Emanuel Sommer, PhD researcher building practical JAX sampling tools; Jakob Robnik, Berkeley physicist developing the Microcanonical Langevin sampler. They discuss scaling Bayesian neural networks, fast sampling tricks and software, microcanonical dynamics, bottlenecks in high dimensions, hybrid warm-start strategies, and tooling for practical uncertainty quantification.
