Alexander Mathis

Expertise AI, machine learning, computational neuroscience, phenotyping, measuring behavior, modeling sensorimotor control, skill learning,  proprioception Alexander Mathis is an assistant professor at the Brain Mind Institute. He is working at the intersection of computational neuroscience, and machine learning, focusing on trying to understand the statistics of behavior and how the brain creates behavior. He studied pure Mathematics at the Ludwig-Maximilians-Universität München, where he also obtained his PhD in computational neuroscience (with Andreas V.M. Herz). During his PhD he developed a theory on how space is represented in the brain. He then was a postdoctoral fellow at Harvard University (with Venkatesh N. Murthy) and the University of Tübingen (with Matthias Bethge) working on a broad range of topics from the sense of smell to computer vision.Since 2020 he is an assistant professor at EPFL, where his group currently works on theories of proprioception and motor control. Additionally, they develop machine learning tools for behavioral analysis (e.g. DeepLabCut, DLC2action, hBehaveMAE, WildCLIP) and conversely try to learn from the brain to solve challenging machine learning problems such as learning motor skills. Indeed with his students, he won competitions based on brain-inspired reinforcement learning algorithms for skill learning (MyoChallenge at NeurIPS 2022, 2023 & 2025). He received numerous prizes and fellowships, incl. the 2024 Robert Bing Prize, 2023 Eric Kandel Young Neuroscientists Prize, and the 2023 Frontiers of Science Award. His earlier work was supported by a Marie Sklodowska-Curie Postdoctoral Fellowship, and a scholarship from the Studienstiftung des deutschen Volkes. Teaching & PhD PhD Students Sepideh Mamooler, Merkourios Simos, Andy Bonnetto, Michal Stanislaw Grudzien, Chengkun Li, Haozhe Qi, Philippe José Emile René Forero, Valentin Alexandre Guy Gabeff, Bianca Ziliotto, Siebe Antonius Martinus Geurts Past EPFL PhD Students Alessandro Marin Vargas, Alberto Chiappa, Stoffl Lucas, Mu Zhou Courses Applied software engineering for life sciences BIO-210 We learn and apply software engineering principles to develop Python projects addressing life science problems. Projects will be expanded iteratively throughout the semester. Brain-like computation and intelligence NX-414 Recent advances in machine learning have contributed to the emergence of powerful models of animal perception and behavior. In this course we will compare the behavior and underlying mechanisms in these models as well as brains. Lecture series on scientific machine learning PHYS-754 This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method. Awards Eric Kandel Young Neuroscientists Prize Hertie Foundation and the Federation of European Neuroscience Societies (FENS) 2023 Robert Bing Prize Swiss Academy of Medical Sciences (SAMS) 2024 Frontiers of Science Award 2018-2022 International Congress for Basic Science 2023