CV

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Education
2006 - 2010 PhD, Computer Sciences (thesis submitted: Mar 2010)
Auckland University of Technology, New Zealand
"Heterogeneous Probabilistic Models for Optimization and Modelling of Evolving Neural Networks"
1999 - 2006 Dipl.-Inf Computer Sciences (Master equivalent)
University of Leipzig, Germany
"Learning in Evolved Neural Networks"
Grade: very good (1.5)
Research Interests
  Evolutionary Computation with focus on Estimation of Distribution Algorithms
Neural Network Models especially Spiking Neurons
Machine Learning
Awards
April 2010 Best Poster Award
New Zealand Computer Science Research Student Conference, Wellington, New Zealand
Dec 2009 Dean's Award for Outstanding Postgraduate Studies
Auckland University of Technology
Jan 2008 Vice Chancellor PhD Scholarship
Auckland University of Technology, 2008-2009
Mar 2008 AUT Lenovo Laptop Scholarship
Auckland University of Technology, 2008
Dec 2006 KEDRI PhD Scholarship
Knowledge Engineering and Discovery Research Institute, 2006-2008
Academic Experience
 

Auckland University of Technology
In my PhD thesis I propose a novel feature selection and classification method employing spiking neural networks and evolutionary algorithms. An evolutionary process evolves appropriate feature subsets for a given classification task and simultaneously optimizes the neural and learning-related parameters of the network. The optimizer belongs to the family of EDA and is based on a heterogeneous probabilistic model that allows the simultaneous exploration of a binary and a continuous search space.

University of Leipzig
In my Master thesis I worked on simple artificial agents exploring a chess board environment. The neural controller of the agent was evolved through a genetic algorithm. Additional Baldwinian learning allowed the agent to detect sudden environmental changes and adapt to them.

Internships

  • Mar-Jul 2003: University Texas Medical Branch in Galveston/Texas/USA
    "Gene Expression Analysis Using Singular Value Decomposition"
Teaching
2010 Teaching Assistant at AUT, Postgraduate Level
Lecture: Data Mining and Knowledge Discovery
Tutorial: Programming I (Java)
2007 Co-supervision of Master Student Project
Inference of Gene Regulatory Networks by means of Probabilistic Incremental Learning
Communication
 

Tutorials
IEEE Joint Conference on Neural Networks, 2009, Atlanta, Georgia,
To Spike or Not to Spike: Methods, Tools and Applications of Spiking Neural Networks

Conference Presentations

  • IEEE Joint Conference on Neural Networks, 2009, Atlanta, Georgia, USA
    Quantum-inspired Feature and Parameter Optimization of Evolving Spiking Neural Networks with a Case Study from Ecological Modelling
  • Intern. Conf. on Neural Information Processing, 2008, Auckland, New Zealand
    Quantum-inspired Feature and Parameter Optimization of Evolving Spiking Neural Network
  • IEEE Congress on Evolutionary Computation, 2007, Singapore
    A Versatile Quantum-inspired Evolutionary Algorithm

Technical Skills
 

Programming
Java, Python, C/C++, Matlab, Basic, Pascal,
furthermore experience with typical development workflows (UML, ER, CVS, etc.),
good experience in web development (PHP, HTML, CSS, mySQL, PostgreSQL)
good skills in LaTeX

Operating Systems
Linux (Ubuntu, Debian, SuSe), Windows
Languages
  German (native speaker)
English (fluent)
Russian (basics)
French (beginner)