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, AmadeusGPT) 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 and 2023). He received numerous prizes and fellowships, incl. the 2024 Robert Bing Prize, 2023 Eric Kandel Young Neuroscientists Prize, 2023 Frontiers of Science Award, a Marie Sklodowska-Curie Postdoctoral Fellowship, and a scholarship from the Studienstiftung des deutschen Volkes.