Experience

Academic positions

 
 
 
 
 

Researcher / Lecturer

TU Chemnitz

Mar 2011 – Present Chemnitz (Germany)

Tenure position in the lab of Artificial Intelligence, Department of Computer Science.

Responsibilities include:

  • Teaching: Machine Learning, Computer Vision, Introduction to AI, Proseminar.
  • Research: computational neuroscience, machine learning.
  • Admin: coordination with SEKO, Erasmus+ coordinator.
 
 
 
 
 

Postdoc

University of Münster

Jul 2006 – Feb 2011 Münster (Germany)

Postdoc at the Institute for Psychology, Westfälische Wilhelms-Universität Münster, lab of Markus Lappe, supervisor Fred Hamker.

Research topics:

  • Basal Ganglia modeling.
  • Reinforcement learning.
 
 
 
 
 

PhD student

Inria Lorraine and Université Henri-Poincaré Nancy-I

Oct 2002 – Jun 2006 Nancy (France)

PhD student at the University Henri-Poincaré Nancy-I and Inria Lorraine (LORIA), supervisors: Frédéric Alexandre and Nicolas Rougier.

  • Emergence of sensorimotor functions on a numerical distributed neural substrate.

Teaching assistant at the University Henri-Poincaré Nancy-I and ESIAL engineering school.

  • Java
  • Computer architecture
  • Project management
  • Artificial Intelligence

Research

A selection of research topics

ANNarchy (Artificial Neural Networks architect) is a general-purpose parallel neuro-simulator for rate-coded or spiking neural networks.

The Basal Ganglia (BG) are the main nuclei involved in reinforcement learning processes in the brain and allow a variety of cognitive functions such as working memory, decision making and action selection.

Deep neural networks and their applications to emotion recognition, attention, sensor fusion…

Modeling the dopaminergic system (VTA, SNc), its afferent system and its influence on the basal ganglia, prefrontal cortex and hippocampus.

The hippocampus is a key structure for mnemonic processes (episodic memory) and spatial navigation. Its importance in model-based behavior is increasingly recognized.

Reinforcement Learning (RL) is a machine learning framework studying how to derive optimal policies from reward signals. Coupled with deep neural networks, it became the most promising approach to artificial intelligence.

Reservoir computing studies the dynamical properties of recurrently connected populations of neurons. Their rich dynamics allow to represent and learn complex tasks currently out of reach of the classical machine learning methods, but also allow to better understand brain activities.

Teaching

Courses taught at the TU Chemnitz

  • Neurocomputing (responsible)

https://www.tu-chemnitz.de/informatik/KI/edu/neurocomputing

  • Deep Reinforcement Learning (responsible)

https://www.tu-chemnitz.de/informatik/KI/edu/deeprl

  • Machine Learning (responsible until 2019)

https://www.tu-chemnitz.de/informatik/KI/edu/ml

  • Computer Vision (responsible until 2019)

https://www.tu-chemnitz.de/informatik/KI/edu/biver

  • Introduction to AI (exercises)

https://www.tu-chemnitz.de/informatik/KI/edu/ki

  • Proseminar AI (responsible)

https://www.tu-chemnitz.de/informatik/KI/edu/prosem

Recent Posts

Successor representations (SR) attract a lot of attention these days, both in the neuroscientific and machine learning / deep RL …

Recent Talks

Artificial Intelligence (AI) has become extremely popular in the last years through the advancement of Deep Learning, a modernized …

Contact

My office is in the StraNa building (in front of the main station), room 1 / 348.