Georgios Exarchakis

Machine Learning and Theoretical Neuroscience Researcher

Institut de la Vision


Georgios is a postdoctoral researcher at Institut de la Vision . Previously, he worked as a postdoctoral researcher at Prof. Dr. Stephane Mallat ’s DATA team at École Normale Supérieure. He received the degree of Dr. rer. nat. from the University of Oldenburg on 01.11.2016. During his doctoral studies, he worked with Prof. Dr. Roland Memisevic at the University of Frankfurt (2012-2013), Prof. Dr. Bruno Olshausen at the Redwood Center for Theoretical Neuroscience at UC Berkeley (2013-2014), and Prof. Dr. Jörg Lücke at the University of Oldenburg(2014-2016). He holds an M.Sc. in Computational Science from the Department for Theoretical Physics of Frankfurt University and a Diploma from the Mathematics Department of the Aristotle University of Thessaloniki.

His research interests revolve around sparse coding, graphical models, signal processing, deep learning, and theoretical neuroscience. He has studied their applications primarily on spike sorting, receptive field estimation, quantum chemistry, and natural image statistics.


  • Artificial Intelligence
  • Machine Learning
  • Theoretical Neuroscience


  • Dr. rer. nat. in Machine Learning, 2016

    Carl von Ossietzky University of Oldenburg

  • M.Sc. in Computational Science, 2012

    Goethe University Frankfurt

  • Dimploma in Mathematics, 2008

    Aristotle University of Thessaloniki


Recent Posts


Kymatio is a Python module for computing wavelet and scattering transforms. It is built on top of PyTorch, but also has a fast CUDA …

Recent Publications

ProSper - A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented …

Truncated Variational Sampling for ‘Black Box’ Optimization of Generative Models

We investigate the optimization of two probabilistic generative models with binary latent variables using a novel variational EM …

Solid harmonic wavelet scattering for predictions of molecule properties

We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory …

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

We introduce a solid harmonic wavelet scattering representation, which is invariant to rigid movements and stable to deformations, for …