Teaching

Notes

Brief notes on specific topics:

• Saddle-point approximations for matrix integrals: the curious case of the Wishart distribution
• A mathematically offensive introduction to SDEs: a bag of tricks and analytical solutions
• The Fokker-Planck Equation: Transporting Probabilities

Python Module of the Week

I used to organize a biweekly session about Python and related topics (repo). My own contributions:

• TensorFlow by example
• NEST tutorial
• GitHub Pull Requests & Code Review
• Organizing, documenting and distributing scientific code
• Advanced Numpy & Pandas
• Jekyll & GitHub Pages

Lectures

Chaos and EI-Balance
Computational Neuroscience, Harvard, Cambridge, MA

Hopfield Model: Saturated Regime and Extensions
Statistical Mechanics of Spin Glasses and Neural Networks, Harvard, Cambridge, MA

Network Models I & II
Introduction to Computational Neuroscience, RWTH Aachen, Germany

Teaching Fellow

Computational Neuroscience (spring 2024)
Harvard, Cambridge, MA

Statistical Mechanics of Spin Glasses and Neural Networks (spring 2023)
Harvard, Cambridge, MA

Teaching Assistant

Theoretical Neuroscience - Fluctuations and Correlations in Neuronal Networks (summer 2020)
RWTH Aachen, Aachen, Germany

Statistical Physics (winter 2016/17)
Humboldt Universität, Berlin, Germany

Tutorials

Introduction to the simulation of structurally detailed large-scale neuronal networks (using NEST)
CNS 2019 Tutorial, Barcelona, Spain (repo)

NEST simulator tutorial
EITN Spring School 2019, Paris, France (repo)

NEST simulator tutorial
EITN Spring School 2018, Paris, France