Abstract
Optical microscopy is widely used to investigate biological samples in vivo. However, microscopic imaging in living tissue—such as the brain—faces multiple challenges. Samples are typically non-transparent and do not lie within a single focal plane; they scatter light, are three-dimensional, and often not stationary at the micrometer scale.
Machine learning offers novel ways to address these longstanding challenges. I will discuss recent work from our group on correcting aberrations and scattering using different approaches, including neural networks and differentiable physics. I will also present tomographic imaging methods for optical microscopy based on Bessel beam —line-shaped focal spots that extend across multiple focal planes.
 

Biosketch
Johannes Seelig studied physics at University of Basel with a minor in molecular biology from 1997-2002. He obtained his PhD in the field of single molecule microscopy from ETH Zurich in 2006. He was a postdoctoral associate (2007-11) and later a research specialist (2011- 2015) at Janelia Research Campus where he worked on neural circuits in Drosophila. From 2016-2025 he was “free-floating” Max Planck Research Group Leader at the Max Planck Institute for Neurobiology of Behavior – caesar.