Curriculum Vitae

I am a Postdoc at the University of Potsdam. I received a Master’s Degree in Computer Science (Diplominformatiker) in 2010 and a Ph.D. (Dr. rer.  nat.) in 2016 from the University of Potsdam. I am interested in machine learning. My current research interests lie in generative adversarial networks, and data science. Machine learning has many diverse applications, and I am working on some of them: computer security (detecting of malware, analysis of encrypted network traffic), precision medicine, and model-building for various applications.

Current Projects

• AEye Junior Research Group: AEye - Artificial Intelligence for Eye Tracking Data: Deep Learning Methods for the Automated Analysis of Cognitive Processes.
• ML-Med: Machine Learning with Relational Background Knowledge for Biomedical Applications

Past Projects

• Malware Detection by Analyzing HTTPS Logs
• Email Security, a project funded by STRATO Rechenzentrum AG.
• Structured In- and Output.

Publications

• Paul Prasse, David Robert Reich, Silvia Makowski, Tobias Scheffer, Lena A. Jäger. Improving Cognitive-State Analysis from Eye Gaze with Synthetic Eye-Movement Data. To appear in Computers & Graphics 2024. (Code).
• Stephan Dominik Kurz, Holger Mahlke, Kathrin Graw , Paul Prasse , Volkmar Falk ,Christoph Knosalla , Andreas Matzarakis. Patterns in acute aortic dissection and a connection to meteorological conditions in Germany. PLOS ONE 2024.
• Silvia Makowski, Paul Prasse, Lena Ann Jäger, Tobias Scheffer. Detection of Drowsiness and Impending Microsleep from Eye Movements. NeurIPS 2023 Workshop Gaze Meets ML 2023.
• Lena Bolliger, David Reich, Patrick Haller, Deborah Jakobi, Paul Prasse, Lena Jäger. ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts. EMNLP 2023. (Code).
• Shuwen Deng, Paul Prasse, David R. Reich,  Tobias Scheffer, Lena A. Jäger. Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding. EMNLP 2023. (Code).
• Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller, Tobias Scheffer, Lena A. Jäger. Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading. ETRA 2023. (Code).
• Paul Prasse, David R. Reich, Silvia Makowski, Seoyoung Ah, Tobias Scheffer, Lena A. Jäger. SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks. ETRA 2023. (Code).
• Daniel G. Krakowczyk, Paul Prasse,  David R. Reich, Sebastian Lapuschkin, Tobias Scheffer,Lena A. Jäger. Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. ETRA 2023. (Code).
• Daniel G. Krakowczyk, David R. Reich, Jakob Chwastek, Deborah N. Jakobi, Paul Prasse,  Assunta Süss, Oleksii Turuta, Pawel Kasprowski,  Lena A. Jäger. pymovements: A Python Package for Eye Movement Data Processing. ETRA 2023. (Code).
• Daniel G. Krakowczyk, David R. Reich, Paul Prasse,  Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger. Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification. NeurIPS 2022. (Code).
• Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, Lena A. Jäger. Detection of ADHD based on Eye Movements during Natural Viewing. ECML 2022.(Video). (Code).
• Paul Prasse, Pascal Iversen, Matthias Lienhard, Kristina Thedinga, Ralf Herwig, Tobias Scheffer. Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction.Cancers 2022, 14(16).
• Paul Prasse, David R. Reich,  Silvia Makowski, Lena A. Jäger, Tobias Scheffer. Fairness in Oculomotoric Biometric Identification. ETRA 2022. (Video). (Code)
• David R. Reich, Paul Prasse, Chiara Tschirner, Patrick Haller, Frank Goldhammer, Lena A. Jäger. Inferring Native and Non-Native Human Reading Comprehension and Subjective Text Difficulty from Scanpaths in Reading. ETRA 2022. (Video). (Code)
• Paul Prasse, Pascal Iversen, Matthias Lienhard, Kristina Thedinga, Chris Bauer, Ralf Herwig, Tobias Scheffer.
  Matching anticancer compounds and tumor cell lines by neural networks with ranking loss.
  NAR Genomics and Bioinformatics 4(1), https://doi.org/10.1093/nargab/lqab128, 2022.
• Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp,  Lukas Bajer and Tobias Scheffer. Learning Explainable Representations of Malware Behavior (Preprint, Online Appendix). ECML 2021.
• Silvia Makowski, Lena A. Jäger, Paul Prasse, Tobias Scheffer. Biometric identification and presentation-attack detection using micro- and macro-movements of the eyes. International Joint Conference on Biometrics, 2020.
• Paul Prasse, Lena A. Jäger, Silvia Makowski, Moritz Feuerpfeil, Tobias Scheffer. On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification. KES 2020. (Online talk)
• Paul Prasse, Lena A. Jäger, Silvia Makowski, Moritz Feuerpfeil and Tobias Scheffer. On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification. International Conference on Knowledge Based and Intelligent Information and Engineering Systems, 2020.
• Paul Prasse, Rene Knaebel, Lukas Machlica, Tomas Pevny and Tobias Scheffer. Joint detection of malicious domains and infected clients. Machine Learning, 2019.
• Lena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler and Tobias Scheffer. Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2019.
• Paul Prasse, Lukas Machlica, Tomas Pevny, Jiri Havelka and Tobias Scheffer. Malware detection by analysing encrypted network traffic with neural networks. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2017.
• Paul Prasse, Lukas Machlica, Tomas Pevny, Jiri Havelka and Tobias Scheffer. Malware detection by analysing network traffic with neural networks. IEEE Security and Privacy Workshops, 2017.
• Paul Prasse, Christoph Sawade, Niels Landwehr and Tobias Scheffer. Learning to Identify Concise Regular Expressions that Describe Email Campaigns.Proceedings of the Journal of Machine Learning Research, Volume 16, 2015.
• Paul Prasse, Christoph Sawade, Niels Landwehr and Tobias Scheffer. Learning to Identify Regular Expressions that Describe Email Campaigns. (with Online Appendix). Proceedings of the 29th International Conference on Machine Learning (ICML-2012), Edinburgh, Scotland, 2012.

Software

• "REx-SVM" - a framework to learn a model that predicts a regular expression that describes the language of a given set of texts (uses the SVM^struct framework from Thorsten Joachims). A download link can be obtained on request to prasse@cs.uni-potsdam.de.

Teaching

• Ringvorlesung Interdisziplinäre angewandte Mathematik (WS 2014/15, Universität Potsdam)
• Vorlesung/Projekt Intelligente Datenanalyse in Matlab (WS 2014/15, Universität Potsdam)
• Vorlesung/Übung Sprachtechnologie (SS 2014, Universität Potsdam)
• Vorlesung/Projekt Intelligente Datenanalyse in Matlab (WS 2013/14, Universität Potsdam)
• Vorlesung Maschinelles Lernen (WS 2013/14, Universität Potsdam)
• Vorlesung/Projekt Intelligente Datenanalyse in Matlab (WS 2012/13, Universität Potsdam)
• Vorlesung Maschinelles Lernen (WS 2012/13, Universität Potsdam)
• Ringvorlesung Interdisziplinäre angewandte Mathematik (WS 2012/13, Universität Potsdam)
• Vorlesung/Übung Sprachtechnologie (SS 2012, Universität Potsdam)
• Vorlesung/Projekt Intelligente Datenanalyse in Matlab (WS 2011/12, Universität Potsdam)
• Vorlesung Maschinelles Lernen (WS 2011/12, Universität Potsdam)
• Vorlesung/Übung Sprachtechnologie (SS 2011, Universität Potsdam)