Alexander Mathis
EPFL SV BMI UPAMATHIS
Campus Biotech
Bâtiment H4
Chemin des mines 9
1202 Genève
+41 21 693 87 21
Office:
H4 3 132.084
Office:
SV 2809
EPFL
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SV
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BMI
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UPAMATHIS
+41 21 693 87 21
EPFL
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SV
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SV-SSV
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SSV-ENS
Web site: Web site: https://sv.epfl.ch/education
Fields of expertise
Biography
Alexander Mathis is an assistant professor at the Brain Mind Institut. 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 and the University of Tübingen (with Venkatesh N. Murthy) 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, we 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 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 Skłodowska-Curie Postdoctoral Fellowship, and a scholarship from the Studienstiftung des deutschen Volkes.
Publications
Selected publications
Alexander Mathis, Pranav Mamidanna, Kevin M. Cury, Taiga Abe, Venkatesh N. Murthy, Mackenzie Weygandt Mathis* & Matthias Bethge* Nature Neuroscience |
DeepLabCut: Markerless tracking of user-defined features with deep learning |
Alessandro Marin Vargas, Axel Bisi, Alberto Silvio Chiappa, Christopher Versteeg, Lee E Miller, Alexander Mathis Cell |
Task-driven neural network models predict neural dynamics of proprioception |
Mu Zhou*, Lucas Stoffl*, Mackenzie W. Mathis, Alexander Mathis International Conference on Computer Vision |
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity |
Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, Venkatesh N Murthy, George Lauder, Catherine Dulac, Mackenzie Weygandt Mathis, Alexander Mathis Nature Methods |
Multi-animal pose estimation, identification and tracking with DeepLabCut |
Alberto Chiappa, Alessandro Marin Vargas, Alexander Mathis NeurIPS |
DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body |
Ying Li, Alexander Mathis, Benjamin Grewe, Jessica A. Osterhout, Biafra Ahanonu, Mark J. Schnitzer, Venkatesh N. Murthy, Catherine Dulac
Cell |
Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice |
Alexander Mathis, Andreas V.M. Herz, Martin Stemmler Neural Computation |
Optimal Population Codes for Space: Grid Cells Outperform Place Cells |
Other publications
Teaching & PhD
Teaching
Life Sciences Engineering