About Me
I am a machine learning researcher with a Ph.D. in computational neuroscience, specializing in models that tackle complex problems at the intersection of AI and neuroscience. I received a bachelor's degree in computer science from University of Ulm before continuing my studies in cognitive science at University of Tübingen. I did my PhD in computational neuroscience at the Kavli Institute for Systems Neuroscience & the University College London and a PostDoc with Mackenzie Mathis at EPFL in Switzerland. Currently looking for new opportunities.
Publications
Time-series attribution maps with regularized contrastive learning
S Schneider, RG Laiz, A Filippova, M Frey, MW Mathis
AISTATS (2025)
Identifiable attribution maps using regularized contrastive learning
S Schneider, RG Laiz, M Frey, MW Mathis
NeurIPS (2024)
CellSeg3D: self-supervised 3D cell segmentation for fluorescence microscopy
C Achard, T Kousi, M Frey, M Vidal, Y Paychère, C Hofmann, A Iqbal, S B Hausmann, S Pagès, MW Mathis
eLife (2024)
NeuroAI: If grid cells are the answer, is path integration the question?
M Frey, MW Mathis, A Mathis
Current Biology 33 (5), R190-R192 (2023)
Probing neural representations of scene perception in a hippocampally dependent task using artificial neural networks
M Frey, CF Doeller, C Barry
CVPR (2023)
Magnetic resonance-based eye tracking using deep neural networks
M Frey, M Nau, CF Doeller
Nature neuroscience 24 (12), 1772-1779 (2021)
Projects
Awesome NeuroAI
A curated list of papers and reviews from the intersection of deep learning and neuroscience. This resource collects significant contributions to the emerging field of NeuroAI.
The Human Move
Quantifying the psychological impact of chess moves using a simple heuristic based on engine evaluations. This project explores the cognitive aspects of decision-making in chess.
Blog Posts
Exploring spatial representations in Visual Foundation Models
An exploration of neural representations in large vision models, with a focus on spatially selective cells that encode parts of the visual field, similar to those found in biological neural networks.
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