Alexander Mathis
Mathis at Harvard University in 2020
Born
Bregenz, Austria
Alma mater
Known forDeepLabCut (deep learning software estimating animal poses)
SpouseMackenzie Weygandt Mathis
Awards
  • DFG postdoctoral fellowship
  • Marie-Curie fellowship
Scientific career
FieldsNeuroscience
InstitutionsÉcole polytechnique fédérale de Lausanne (EPFL)
Websitehttps://www.mathislab.org/

Alexander Mathis is an Austrian mathematician, computational neuroscientist and software developer. He is currently an assistant professor at the École polytechnique fédérale de Lausanne (EPFL) in Switzerland. His research interest focus on research at the intersection of computational neuroscience and machine learning.

Education

Mathis studied mathematics, logic and theory of science at the Ludwig Maximilians University of Munich, Germany.[1] His interest in computing and cryptography led him to pursue a PhD in computational neuroscience at the Graduate School for Systemic Neuroscience under the supervision of Prof. Andreas Herz at the department of neurobiology at the Ludwig Maximilians University of Munich.[2] During his PhD work, he studied optimal coding approaches to reveal the properties of grid cells[3] and how distributed population activity readout can be implemented in plausible bio-physical models.[4] The predictions of this theory were confirmed in rats by the Moser laboratory[5] and artificial systems optimized for navigation by DeepMind.[6]

He spent an exchange year at the Autonomous University of Barcelona in Spain.[1]

Career and research

After completing his PhD, Mathis went in 2013 as a postdoctoral fellow to work under the mentor-ship of Prof. Venkatesh N. Murthy at the Department of Molecular and Cellular Biology at Harvard University.[7][8] In addition, in 2015, he joined the research group of Prof. Matthias Bethge at the Bernstein Center for Computational Neuroscience in Tübingen and the University of Tübingen in Germany.[9] His postdoctoral research positions were funded by a DFG postdoctoral fellowship[10] and a Marie-Curie fellowship.[11]

Mathis conducted research in odor-guided navigation, social behaviors, motor learning, and the cocktail party problem.[12] He employed deep learning methods and experimentally testable computational models to study animal behavior and neural data. He has developed tools such as DeepLabCut[13] and DeepDraw[14] to accurately measure animal and human behavior.[15] He is one of the initiators and developers of the open-source research tool DeepLabCut with his spouse Mackenzie Weygandt Mathis that estimates animal postures via computer vision and machine learning.[16] Mathis has also created models and theories on adaptive behavior, in particular on motor control and sensorimotor transformations.[17] Several publications appeared during this research period, including the highly cited paper "DeepLabCut: markerless pose estimation of user-defined body parts with deep learning" by Mathis et al. published in 2018 in Nature Neuroscience.[18]

In August 2020, he moved as an assistant professor to the École polytechnique fédérale de Lausanne (EPFL) in Switzerland where he started his own research laboratory "the Mathis Group", dedicated to research at the intersection of computational neuroscience and machine learning. The Mathis Group is committed to enhancing machine learning tools for animal behavior analysis and to developing of neural network models of sensorimotor representation.[19]

His research was featured in The Atlantic,[20] Nature,[21] and Quanta Magazine,.[22]

Awards and grants

Chan Zuckerberg Initiative (CZI) awarded funding for Mathis' open source project DeepLabCut.[23] Mathis further was awarded with a postdoctoral fellowship by the Deutsche Forschungsgemeinschaft[10] and a Marie Skłodowska-Curie Actions fellowship by the European Union[11]

Publications

Alexander Mathis publications indexed by Google Scholar

Personal life

Mathis is married to fellow neuroscientist Prof. Dr. Mackenzie Weygandt Mathis, who is also the developer of DeepLabCut and an Assistant Professor at the École polytechnique fédérale de Lausanne (EPFL).[24]

References

  1. 1 2 "Alexander Mathis | Campus Biotech". www.campusbiotech.ch. Retrieved 2020-06-12.
  2. ↑ "Alumni / Former lab members of Andreas Herz". Retrieved 2020-06-12.
  3. ↑ Mathis, Alexander; Herz, Andreas V. M.; Stemmler, Martin (2012). "Optimal Population Codes for Space: Grid Cells Outperform Place Cells" (PDF). Neural Computation. 24 (9): 2280–2317. doi:10.1162/NECO_a_00319. PMID 22594833. S2CID 15755674.
  4. ↑ Stemmler, Martin; Mathis, Alexander; Herz, Andreas V. M. (2015-12-01). "Connecting multiple spatial scales to decode the population activity of grid cells". Science Advances. 1 (11): e1500816. Bibcode:2015SciA....1E0816S. doi:10.1126/science.1500816. ISSN 2375-2548. PMC 4730856. PMID 26824061.
  5. ↑ Stensola, Hanne; Stensola, Tor; Solstad, Trygve; Frøland, Kristian; Moser, May-Britt; Moser, Edvard I. (2012-12-05). "The entorhinal grid map is discretized". Nature. 492 (7427): 72–78. Bibcode:2012Natur.492...72S. doi:10.1038/nature11649. ISSN 1476-4687. PMID 23222610. S2CID 4398517.
  6. ↑ "Navigating with grid-like representations in artificial agents". Deepmind. Retrieved 2020-08-31.
  7. ↑ VN, Zeitungsimport. "Unterrichten und forschen an der Elite-Uni Harvard". Vorarlberger Nachrichten | VN.at (in German). Retrieved 2020-06-12.
  8. ↑ "People". MurthyLab @Harvard. 2016-06-11. Retrieved 2020-06-12.
  9. ↑ "BETHGE LAB · People". bethgelab.org. Retrieved 2020-06-12.
  10. 1 2 "DFG - GEPRIS - Dr. Alexander Mathis". gepris.dfg.de. Retrieved 2020-06-12.
  11. 1 2 "BETHGE LAB · Funding". bethgelab.org. Retrieved 2020-06-12.
  12. ↑ Mathis, Alexander; Rokni, Dan; Kapoor, Vikrant; Bethge, Matthias; Murthy, Venkatesh N. (2016-09-01). "Reading Out Olfactory Receptors: Feedforward Circuits Detect Odors in Mixtures without Demixing". Neuron. 91 (5): 1110–1123. doi:10.1016/j.neuron.2016.08.007. PMC 5035545. PMID 27593177.
  13. ↑ "DeepLabCut". adaptive motor control lab. Retrieved 2020-06-13.
  14. ↑ Sandbrink, Kai J.; Mamidanna, Pranav; Michaelis, Claudio; Mathis, Mackenzie Weygandt; Bethge, Matthias; Mathis, Alexander (2020-05-08). "Task-driven hierarchical deep neural network models of the proprioceptive pathway". doi:10.1101/2020.05.06.081372. S2CID 218582542. {{cite journal}}: Cite journal requires |journal= (help)
  15. ↑ Mathis, Mackenzie Weygandt; Mathis, Alexander (2019-11-29). "Deep learning tools for the measurement of animal behavior in neuroscience". Current Opinion in Neurobiology. 60: 1–11. arXiv:1909.13868. doi:10.1016/j.conb.2019.10.008. PMID 31791006. S2CID 203593843.
  16. ↑ "AlexEMG - Overview". GitHub. Retrieved 2020-06-12.
  17. ↑ Herz, Andreas VM; Mathis, Alexander; Stemmler, Martin (2017-09-06). "Periodic population codes: From a single circular variable to higher dimensions, multiple nested scales, and conceptual spaces". Current Opinion in Neurobiology. 46: 99–108. doi:10.1016/j.conb.2017.07.005. PMID 28888183. S2CID 22556840.
  18. ↑ Mathis, Alexander; Mamidanna, Pranav; Cury, Kevin M.; Abe, Taiga; Murthy, Venkatesh N.; Mathis, Mackenzie Weygandt; Bethge, Matthias (2018-08-20). "DeepLabCut: markerless pose estimation of user-defined body parts with deep learning". Nature Neuroscience. 21 (9): 1281–1289. doi:10.1038/s41593-018-0209-y. ISSN 1097-6256. PMID 30127430. S2CID 4748395.
  19. ↑ "Mathis Group". Mathis Group. Retrieved 2020-06-12.
  20. ↑ Young, Ed (3 July 2018). "A Game-Changing AI Tool for Tracking Animal Movements". The Atlantic. Retrieved 2020-10-10.
  21. ↑ Kwok, Roberta (2019-09-30). "Deep learning powers a motion-tracking revolution". Nature. 574 (7776): 137–138. Bibcode:2019Natur.574..137K. doi:10.1038/d41586-019-02942-5. PMID 31570871. S2CID 203592858.
  22. ↑ Cepelewicz, Jordana (10 December 2019). "To Decode the Brain, Scientists Automate the Study of Behavior". Quanta Magazine. Retrieved 2020-10-10.
  23. ↑ "Researchers awarded for open-source software projects". Harvard Gazette. 2019-11-18. Retrieved 2020-08-31.
  24. ↑ Evangelista, Sandy (27 September 2019). "Nominations of EPFL professors". Retrieved 2020-06-12.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.