FH-Prof. Priv.-Doz. Dipl.-Ing. Mag. Dr. Matthias Zeppelzauer
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Head of
Media Computing Research Group
Institute of Creative\Media/Technologies - Department of Media and Digital Technologies
Study programmes
- Data Science and Business Analytics (BA)
- Data Intelligence (MA)
- Smart Engineering (BA)
- Media Technology (BA)
- Creative Computing (BA)
Departments
- Media and Digital Technologies
- Computer Science and Security
Short profile
- Since 2013 Senior Researcher at the Media Computing Group, Institute of Creative\Media/Technologies, St. Poelten University of Applied Sciences
- 2011 – 2015 Postdoctoral researcher at the Interactive Media Systems Group, Vienna University of Technology, Austria
- 2011 PhD degree in Computer Science from Vienna University of Technology, Austria, Thesis title: Syntactic and Semantic Concepts in Audio-Visual Media, with highest distinction.
- 2005 – 2011 Predoctoral researcher at the Interactive Media Systems Group, Vienna University of Technology, Austria
- 2006 Master of Science in Business Informatics from Vienna University of Technology, Austria, with highest distinction.
- 2005 Master of Science in Computer Graphics & Digital Image Processing from Vienna University of Technology, Austria, with highest distinction.
Projects
- Trust AI - A platform for interactive and trustworthy artificial intelligence.
- EyeQTrack - Quantitative Eye-Tracking Analytics for Adaptive XR Training & Rehabilitation in Healthcare
- AniVision
- deepForce - Pushing the limits of rapid estimation of knee joint contact force estimation in clinical gait analysis by Machine and Deep Learning
- BIMCheck – Intelligent and Automated Target-Performance Comparisons in the Building Industry.
- FIVE - #Fitspiration Image VErification
- CounterSpeech - Young People Against Online Hate
- IMREA - Intelligent Multimodal Real Estate Assessment
- QUAKE-iP - Quality Control and Evaluation in Production
- HIPstar
- IntelliGait 3D- Gait Data Mining
- Scribe ID AI
- Plant Monitoring AI
- ReMoCap-Lab
- QuickSpeech - Automatic Question and Answer Generation
- SAiEX - Safe Artificial Intelligence with integrated explainable integrity level
- Ressel Center: Horizons of personalized music therapy 2
- Big-Data Analytics
- Data Science Bootcamp
- WiKant-Knowledge-based production of profiles
- ImmBild - Location Assessment by Computer Vision
- SoniControl 2.0 - The first Ultrasonic Firewall
- SAMBA - Smart Data for Music Business Administration
- 360 AI
- InfraBase - Automatic Building Footprint Segmentation
- IntelliGait – Intelligent Gait Analysis
- Object Recognition for Indoor Navigation
- SoniTalk - an Open Protocol for Data-over-Sound
- ImmoAge - Visual Age Prediction of Real Estate
- Ressel Center: Horizons of personalized music therapy 1
- SoniControl - the first ultrasonic firewall
- Pitoti 3D
- Digital interactive display window application
- Deep Tagging - Automatic Image Tagging by Deep Learning
Selected Publications
Slijepcevic, D., Horst, F., Lapuschkin, S., Horsak, B., Raberger, A.-M., Kranzl, A., Samek, W., Breitender, C., Schöllhorn, W., & Zeppelzauer, M. (2022). Explaining Machine Learning Models for Clinical Gait Analysis. ACM Transactions on Computing for Healthcare, 3(2), 14:1-14:27. https://doi.org/10/gnt2s9
Baumhauer, T., Slijepcevic, D., & Zeppelzauer, M. (2022). Bounded logit attention: Learning to explain image classifiers. Proceedings of the Attention Workshop, NeurIPS 2022, New Orleans, USA. https://arxiv.org/pdf/2105.14824
Baumhauer, T., Schöttele, P., & Zeppelzauer, M. (2022). Machine Unlearning: Linear Filtration for Logit-based Classifiers. Journal on Machine Learning. https://doi.org/10.1007/s10994-022-06178-9
Slijepčević, D., Henzl, M., Klausner, L. D., Dam, T., Kieseberg, P., & Zeppelzauer, M. (2021). k‑Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers. Computers & Security, 111, 19. https://doi.org/10.1016/j.cose.2021.102488
Bernard, Jürgen, Hutter, M., Sedlmair, M., Zeppelzauer, Matthias, & Munzner, Tamara. (2021). A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3–4), 1–42. https://doi.org/10/gnt2wf
Kirchknopf, A., Slijepcevic, D., & Zeppelzauer, M. (2021). Multimodal Detection of Information Disorder from Social Media. International Conference on Content-Based Multimedia Indexing (CBMI), 4. https://doi.org/10/gmxnm5
Bernard, J., Hutter, M., Zeppelzauer, M., Sedlmair, M., & Munzner, T. (2021). ProSeCo: Visual analysis of class separation measures and dataset characteristics. Computers & Graphics, 96, 48–60. https://doi.org/https://doi.org/10.1016/j.cag.2021.03.004
Zielinski, B., Lipinski, M., Juda, M., Zeppelzauer, Matthias, & Dlotko, Pawel. (2021). Persistence Codebooks for Topological Data Analysis. Journal of Artificial Intelligence Review, 54, 1969–2009. https://doi.org/https://doi.org/10.1007/s10462-020-09897-4
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., Worisch, M., & Zeppelzauer, M. (2020). GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait. Scientific Data, 7:143(1), 1–8. https://doi.org/10/gh372d
Bernard, Jürgen, Hutter, M., Sedlmair, M., Zeppelzauer, Matthias, & Munzner, Tamara. (2019). A taxonomy of property measures to support the explainability of the interactive data labeling process. ACM Transactions on Interactive Intelligent Systems (TiiS), Submitted.
Zielinski, B., Lipinski, Michal, Juda, M., Zeppelzauer, M., & Dlotko, Pawel. (2019). Persistence Bag-of-Words for Topological Data Analysis. Proceedings of the International Joint Conference on Artificial Intelligence 2019, 6. https://doi.org/10/ghpp7z
Wagner, M., Slijepcevic, D., Horsak, B., Rind, A., Zeppelzauer, M., & Aigner, W. (2018). KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis. IEEE Transactions on Visualization and Computer Graphics (TVCG), 25(3), 1528–1542. https://doi.org/10/ghppzn
Slijepcevic, D., Zeppelzauer, M., Raberger, A.-M., Schwab, C., Schuller, M., Baca, A., Breiteneder, C., & Horsak, B. (2018). Automatic Classification of Functional Gait Disorders. IEEE Journal of Biomedical and Health Informatics, 5(22), 1653–1661. https://doi.org/10/ghz24w
Bernard, J., Zeppelzauer, M., Sedlmair, M., & Aigner, W. (2018). VIAL – A Unified Process for Visual-Interactive Labeling. The Visual Computer, 34(1189), 16. https://doi.org/10/gd5hr3
Zeppelzauer, M., Despotovic, M., Sakeena, M., Koch, D., & Döller, M. (2018). Automatic Prediction of Building Age from Photographs. Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR "18), 126–134. https://doi.org/10/ghpp2k
Zeppelzauer, M., Zielinski, B., Juda, M., & Seidl, M. (2018). A Study on Topological Descriptors for the Analysis of 3D Surface Texture. Journal on Computer Vision and Image Understanding (CVIU), 167, 74–88. https://doi.org/10/ghpp2h
Bernard, J., Zeppelzauer, M., Lehmann, M., Müller, M., & Sedlmair, M. (2018). Towards User-Centered Active Learning Algorithms. Computer Graphics Forum, 37, 121–132. https://doi.org/10/gdw79h
Bernard, Jürgen, Hutter, M., Zeppelzauer, M., Fellner, D., & Sedlmair, M. (2017). Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study. IEEE Transactions on Visualization and Computer Graphics (TVCG), 24(1). https://doi.org/10/gcqb3r
Zeppelzauer, M., & Schopfhauser, D. (2016). Multimodal classification of events in social media. Image and Vision Computing. https://doi.org/10/ghpp2q
Zaharieva, M., Del Fabro, M., & Zeppelzauer, M. (2015). Cross-Platform Social Event Detection. IEEE Multimedia, 22(3), 14. https://doi.org/10/gh3773
Salvador, A., Zeppelzauer, M., Manchón-Vizuente, D., Calafell, A., & Giró-i-Nieto, X. (2015, April 28). Cultural Event Recognition with Visual ConvNets and Temporal Models. Proceedings of the CVPR Workshop ChaLearn Looking at People 2015. Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, United States. http://arxiv.org/abs/1504.06567
Zaharieva, M., Zeppelzauer, M., Del Fabro, M., & Schopfhauser, D. (2015, March 24). Social Event Mining in Large Photo Collections. Proceedings of the International Conference on Multimedia Retrieval. ACM International Conference on Multimedia Retrieval, Shanghai, China.
Stöger, A., Heimann, G., Zeppelzauer, M., Ganswindt, A., Hensman, S., & Charlton, B. (2012). Visualizing Sound Emission of Elephant Vocalizations: Evidence for Two Rumble Production Types. Plos One, 7(11:e48907). http://dx.plos.org/10.1371/journal.pone.0048907
Mitrović, D., Zeppelzauer, M., & Breiteneder, C. (2010). Features for Content-Based Audio Retrieval. In Advances in Computers (Vol. 78, pp. 71–150). Burlington: Academic Press. http://www.sciencedirect.com/science/article/pii/S0065245810780037