Faculty and Staff — Computer Science

• Computer Science Project
• Mathematics for Computer Science

Anna Beer, PhD has been an Assistant Professor of Computer Science at Webster Vienna Private University since March 2026. She previously worked as a postdoctoral researcher at the University of Vienna and at Aarhus University in Denmark. She earned her doctorate in Computer Science from LMU Munich in 2021.

Her research focuses on fundamental problems in data mining and unsupervised machine learning, with an emphasis on clustering and evaluation methods. While developing theoretically grounded solutions to complex problems is a core aspect of her work, she also pursues interdisciplinary projects. At Aarhus University, she worked on methods for analyzing molecular dynamics data, and her current research addresses challenges arising in astrophysical data.

Her research interests include clustering — with a focus on density-based, spectral and hierarchical methods — alongside unsupervised machine learning, fairness in machine learning, and the evaluation of data mining, machine learning and artificial intelligence.

• Beer, A., Krieger, L., Weber, P., Ritzert, M., Assent, I., & Plant, C. (2026). Internal Evaluation of Density-Based Clusterings with Noise. In: International Conference on Learning Representations (in press) (2026). [Preprint Link]
• Krieger, L., Beer, A., Matthews, P., Thiesson, A. M., & Assent, I. (2025). FairDen: Fair Density-based Clustering. In The Thirteenth International Conference on Learning Representations. Vol. 2025. 2025, pp. 101017–101036. [Paper Link]
• Draganov, A. A., Weber, P., Jørgensen, R. S. M., Beer, A., Plant, C., & Assent, I. (2025). Ultrametric Cluster Hierarchies: I Want ‘em All!. In Advances in Neural Information Processing Systems 2025. [Paper Link]
• Beer, A., Weber, P., Miklautz, L., Leiber, C., Durani, W., Böhm, C., & Plant, C. (2024, December). Shade: Deep density-based clustering. In 2024 IEEE International Conference on Data Mining (ICDM) (pp. 675-680). IEEE. [DOI Link]
• Beer, A., Draganov, A., Hohma, E., Jahn, P., Frey, C. M., & Assent, I. (2023, August). Connecting the Dots--Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering. In Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining (pp. 80-92). [DOI Link]
• Ullmann, T., Beer, A., Hünemörder, M., Seidl, T., & Boulesteix, A. L. (2023). Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study. Advances in Data Analysis and Classification, 17(1), 211-238. [DOI Link]

See More Publications

Email: anna.beer@webster.ac.at

• STAT 1100/50 Descriptive Statistics
• BUSN 3950/50 Business Analytics

Gudrun Kinz is a predoctoral researcher in Computer Science at Webster Vienna Private University. Her work is grounded in mathematical modeling, engineering problem-solving and the development of robust technical systems for real-world applications. She holds a master's degree in Technical Mathematics from TU Wien, with a focus on numerical methods, differential equations, mechanics, and the modeling and simulation of dynamical systems.

Her background includes hands-on technical training as a tool engineer, as well as extensive experience in mechanical engineering, simulation and teaching. Before beginning her doctoral research, she worked on modeling, simulation, validation and parameter estimation for dynamic mechanical and hydromechanical systems in academia and the automotive and aerospace industries.

Her current doctoral research applies this perspective to computer vision, addressing a practical challenge in wheat breeding: the reliable acquisition and automated analysis of high-resolution drone imagery for assessing Fusarium head blight, a fungal disease affecting wheat. The work combines drone-based image acquisition, camera geometry and calibration, oblique imaging, and robust image analysis under changing field conditions.

• Kinz, G., Roth, P. M., Bürstmayr, H. (2024) Capturing high-resolution drone images for wheat classification, in Austrian Symposium on AI, Robotics, and Vision.
• Dolin, V. M., Kinz, G., Roth, P. M. (2025) AI-driven Optimization of Veterinary Care Services, in EAAP Conference on Artificial Intelligence in Animal Science.
• Valentina Dolin and Gudrun Kinz and Martina Jezik and Peter M. Roth (2025) Echtzeitvermittlung von veterinärmedinizschen Leistungen}, in ALVA-Tagung.
• Dolin, V., Kinz, G., Jezik, M., Kramer, M. A. M., Roth, P. M. (2026) Interactive VetMap of Austria, in Austrian Symposium on AI, Robotics, and Vision.

Email: gudrun.kinz@webster.ac.at 

• COSC 1550 Computer Programming
• COSC 2010 Computer Topics
• COSC 3910 Project
• MATH 2200 Statistic
• MBA 5020 Quantitative Methods for the MBA
• STAT 1100 Descriptive Statistics

Peter M. Roth, PhD, is a full professor of Computer Science at Webster Vienna Private University. He earned his doctorate from Graz University of Technology in January 2008 and completed his habilitation in Applied Computer Science at the same institution in December 2022. He also holds a master's degree in Mathematics from Graz University of Technology.

His research focuses on machine learning, pattern recognition, statistical methods, image processing and computer vision, with particular emphasis on applying computational approaches to veterinary medicine and the life sciences. He has accumulated approximately 9,800 scientific citations, with an h-index of 36.

Roth is a senior member of the IEEE and serves as chairman of the Austrian Association of Pattern Recognition, a position he has held since 2013.

Before joining WVPU in 2025, he served as a professor at the University of Veterinary Medicine, Vienna, a guest professor at the Technical University of Munich's International AI Future Lab, a university assistant at Graz University of Technology, and a scientist at the Austrian Institute of Technology. He has also held external lecturing positions at FH Wiener Neustadt since 2017 and FH Joanneum since 2015.

His honors include the Best Conference Paper Award at the International Symposium on Mixed and Augmented Reality in 2021, the Best Presentation Award at the Computer Vision Winterworkshop in 2019, and the Best Paper Award at the Asian Conference on Computer Vision in 2012. He has co-authored more than 150 peer-reviewed publications and holds multiple U.S. patents in the areas of pose estimation and semantic segmentation.

• Peter M. Roth, Martin Hirzer, Martin Köstinger, Csaba Beleznai, and Horst Bischof. Person Re-Identification, chapter Mahalanobis Distance Learning for Person Re-Identification, pages 249–270. Springer, 2014.
• Martin Godec, Peter M. Roth, and Horst Bischof. Hough-based tracking of non-rigid objects. Computer Vision and Image Understanding, 117(10):1245–1256, 2013.
• Peter M. Roth, Sabine Sternig, and Horst Bischof. Advanced Topics in Computer Vision and Pattern Recognition, chapter Learning Object Detectors in Stationary Environments, pages 377–409. Springer, 2013.
• Martin Hirzer, Peter M. Roth, Martin Köstinger, and Horst Bischof. Relaxed pairwise learned metric for person re-identification. In Proc. European Conf. on Computer Vision, 2012.
• Martin Köstinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof. Large scale metric learning from equivalence constraints. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2012.
• Martin Köstinger, Peter M. Roth, and Horst Bischof. Synergy-based learning of facial identity. In Proc. DAGM-OAGM Symposium, 2012. (Main Prize).
• Martin Köstinger, Paul Wohlhart, Peter M. Roth, and Horst Bischof. Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies, 2011.
• Paul Wohlhart, Michael Donoser, Peter M. Roth, and Horst Bischof. Detecting partially occluded objects with an implicit shape model random field. In Proc. Asian Conf. on Computer Vision, 2012. (Best Paper Award).
• Sabine Sternig, Peter M. Roth, and Horst Bischof. Inverse multiple instance learning for classifier grids. In Proc. Int'l Conf. on Pattern Recognition, 2010. (Best Scientific Paper Award).
• Peter M. Roth and Martin Winter. Survey of appearance-based methods for object recognition. Technical Report ICG-TR-08/01, Graz University of Technology, Institute for Computer Graphics and Vision, 2008.

Email: peter.roth@webster.ac.at