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Mag. Dipl.Ing. Dr.

Matthias Zeppelzauer is researcher in data science and machine learning and works as a professor and senior researcher at the Institute of Creative Media Technologies at St. Poelten University of Applied Sciences, since 2013.

From 2011 to 2015, he worked as a postdoctoral researcher at the Interactive Media Systems Group at the Vienna University of Technology in the areas of content-based audio and visual retrieval, computer vision, and machine learning. He graduated with Master of Sciences at Vienna University of Technology in Computer Science and Business Informatics in 2005 and 2006, respectively, and holds a PhD in Computer Science from the Vienna University of Technology. Matthias Zeppelzauer is involved in the acquisition and execution of a numerous basic and applied research projects at national and international levels. He is a lecturer for undergraduate- and graduate programs with an emphasis on signal processing and content-based retrieval. Matthias Zeppelzauer received several performance scholarships from the Vienna University of Technology and was awarded by the Austrian Computer Society for outstanding achievements in the area of pattern recognition.

Research Interests: pattern recognition · computer vision · machine learning · deep learning · data mining · multimodal retrieval· social media analysis

Research Activities:

Recent Publications

Despotovic, M., D. Koch, S. Leiber, M. Döller, M. Sakeena, and M. Zeppelzauer, "Prediction and Analysis of Heating Energy Demand and Year of Construction for Detached Houses by Computer Vision", Energy, Submitted.
Koch, D., M. Despotovic, S. Leiber, M. Sakeena, M. Döller, and M. Zeppelzauer, "Real Estate Image Analysis - A Literature Review", Real Estate Economics Journal, Submitted.
Musik, C., and M. Zeppelzauer, "Understanding Image Processing Algorithms: The Sociotechnical Construction of the Ground Truth", Submitted to VIEW Journal of European Television History and Culture, Submitted.
Slijepcevic, D., M. Zeppelzauer, A-M. Raberger, C. Schwab, M. Schüller, A. Baca, and C. Breiteneder, "Automatic Classification of Functional Gait Disorders", IEEE Journal of Biomedical and Health Informatics, vol. 22, issue 5, pp. 1653 - 1661, 09/2018.
Zielinski, B., M. Juda, and M. Zeppelzauer, "Persistence Codebooks for Topological Data Analysis", arXiv preprint arXiv:1802.04852, 2018.

Press articles