Prof Dr Michael Wand (University of Mainz): Deep Learning, the Universe and all the Rest
A Personal Perspective on Interdisciplinary Research in Machine Learning
20 March 2025, 16.00 h in PB-H 0103
The talk is part of the ZESS lecture series and hosted by the DFG research unit “Learning to Sense” (L2S).
In this talk, Prof Wand will give an overview of activities in his group as well as touch upon the broader architecture of the research landscape at JGU Mainz, where computer science is positioned in a strongly interdisciplinary context within the natural sciences. In terms of the latter, he will discuss several research initiatives that aim at strengthening both fundamental research in machine learning as well as applying machine learning methods to advance our understanding of complex physical systems. In terms of the former, he will discuss how we try to understand better how and why deep learning works so well. As a satisfactory answer to this question is still an open problem at large, my presentation of our work will have to consist of a collection of disparate pieces to the puzzle, such as how to exploit symmetry in deep learning, how seemingly minor tweaks such as batch normalization can lead to complex dynamical effects in numerical optimization, and how discrete model systems might be able to give us hints towards generic priors of natural data, overall providing a subjective summary of unexpected challenges and opportunities.
Michael Wand is professor of computer science at Johannes Gutenberg-Universität Mainz, working at the intersection of computer graphics, vision and machine learning and speaker of the research center “Algorithmic Intelligence as Emergent Phenomenon” at JGU Mainz. He has previously held a faculty position at Utrecht University and a junior group leader position at Saarland University/MPI Informatics Saarbrücken. He obtained a diploma in computer science from Paderborn University, a doctoral degree in computer graphics from Tübingen University and has been a postdoc visitor at Stanford University. His current research focuses on interdisciplinary applications and development of machine learning methods (mostly in computational physics). He would also really like to understand better how and why deep learning actually works so well.
