René Raab

Chair of Machine Learning and Data Analytics

Alumni

Contact

Email: rene.raab@fau.de

My time at FAU will end soon. Unfortunately, I therefore do not have the necessary capacities to supervise student theses at this time. Thus, please refrain from inquiries regarding the supervision of (both internal and external) theses.

New LMU contact: Rene.Raab@lmu.de

since 10/2025	Researcher Ludwig-Maximilians-Universität München (LMU)
09/2019-10/2025	Researcher Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
since 09/2019	PhD Candidate Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
09/2017-07/2019	M.Sc. in Artificial Intelligence Maastricht University, The Netherlands
08/2016-01/2017	Erasmus exchange semester Aarhus University, Denmark
10/2014-08/2017	B.Sc. in Kognitive Informatik Universität Bielefeld, Germany

Semester	Responsibilities
Winter 2023/2024	Green AI - AI for Sustainability and Sustainability of AI (Seminar)
Summer 2023	Green AI - AI for Sustainability and Sustainability of AI (Seminar)
Winter 2022/2023	Green AI - AI for Sustainability and Sustainability of AI (Seminar)
Summer 2022	Green AI - AI for Sustainability and Sustainability of AI (Seminar)
Summer 2021	Algorithmen und Datenstrukturen (für Medizintechnik) - Übung
Winter 2020/21	Algorithmen und Datenstrukturen (für Medizintechnik) - Übung
Summer 2020	Algorithmen und Datenstrukturen (für Medizintechnik) - Übung
Winter 2019/20	Algorithmen und Datenstrukturen (für Medizintechnik) - Übung

2025

Raab R., Berrang P., Gerhart P., Schröder D.:
SoK: Descriptive Statistics Under Local Differential Privacy
In: Proceedings on Privacy Enhancing Technologies 2025 (2025), p. 118-149
ISSN: 2299-0984
DOI: 10.56553/popets-2025-0008
URL: https://petsymposium.org/popets/2025/popets-2025-0008.php
BibTeX: Download
Raab R., Bohr A., Klede K., Gmeiner B., Eskofier B.:
Estimating Group Means Under Local Differential Privacy
In: Proceedings on Privacy Enhancing Technologies 2025 (2025), p. 236-274
ISSN: 2299-0984
DOI: 10.56553/popets-2025-0129
URL: https://petsymposium.org/popets/2025/popets-2025-0129.php
BibTeX: Download

2024

Schrupp B., Klede K., Raab R., Eskofier B.:
Simulation and Detection of Healthcare Fraud in German Inpatient Claims Data
International Conference on Computational Science 2024
In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (ed.): Computational Science -- ICCS 2024, Cham: 2024
DOI: 10.1007/978-3-031-63772-8_22
BibTeX: Download
Zürl M., Stoll P., Brehm I., Sueskind J., Raab R., Petermann J., Zanca D., Simon R., von Fersen L., Eskofier B.:
Automated long-term monitoring of stereotypical movement in polar bears under human care using machine learning
In: Ecological Informatics (2024), p. 102840
ISSN: 1574-9541
DOI: 10.1016/j.ecoinf.2024.102840
BibTeX: Download

2023

Abdullahi Yari I., Raab R., Küderle A., Eskofier B.:
Aligning Federated Learning with Existing Trust Structures in Health Care Systems
In: International Journal of Environmental Research and Public Health 20 (2023), Article No.: 5378
ISSN: 1661-7827
DOI: 10.3390/ijerph20075378
URL: https://www.mdpi.com/1660-4601/20/7/5378
BibTeX: Download
Raab R., Küderle A., Zakreuskaya A., Stern AD., Klucken J., Kaissis G., Rueckert D., Boll S., Eils R., Wagener H., Eskofier B.:
Federated electronic health records for the European Health Data Space
In: The Lancet Digital Health 5 (2023), p. e840-e847
ISSN: 2589-7500
DOI: 10.1016/S2589-7500(23)00156-5
URL: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(23)00156-5/fulltext
BibTeX: Download
Schwinn L., Raab R., Nguyen A., Zanca D., Eskofier B.:
Exploring misclassifications of robust neural networks to enhance adversarial attacks
In: Applied Intelligence (2023)
ISSN: 0924-669X
DOI: 10.1007/s10489-023-04532-5
BibTeX: Download

2022

Schwinn L., Bungert L., Nguyen A., Raab R., Pulsmeyer F., Precup D., Eskofier B., Zanca D.:
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Proceedings of the 39th International Conference on Machine Learning (Baltimore, USA, 17. July 2022 - 23. July 2022)
In: PMLR (ed.): 162 2022
Open Access: https://proceedings.mlr.press/v162/schwinn22a.html
URL: https://proceedings.mlr.press/v162/schwinn22a.html
BibTeX: Download
Zürl M., Stoll P., Brehm I., Raab R., Zanca D., Kabri S., Happold J., Nille H., Prechtel K., Wünsch S., Krause M., Seegerer S., von Fersen L., Eskofier B.:
Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
In: Animals 12 (2022), p. 692
ISSN: 2076-2615
DOI: 10.3390/ani12060692
BibTeX: Download

2021

Bungert L., Raab R., Roith T., Schwinn L., Tenbrinck D.:
CLIP: Cheap Lipschitz Training of Neural Networks
International Conference on Scale Space and Variational Methods in Computer Vision
In: Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon (ed.): SSVM 2021: Scale Space and Variational Methods in Computer Vision, Cham: 2021
DOI: 10.1007/978-3-030-75549-2_25
URL: https://arxiv.org/abs/2103.12531
BibTeX: Download
Schwinn L., Nguyen A., Raab R., Bungert L., Tenbrinck D., Zanca D., Burger M., Eskofier B.:
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
Conference on Uncertainty in Artificial Intelligence (UAI) (Online, 27. July 2021 - 30. July 2021)
URL: https://arxiv.org/abs/2102.12196
BibTeX: Download
Schwinn L., Nguyen A., Raab R., Zanca D., Eskofier B., Tenbrinck D., Burger M.:
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
International Joint Conference on Neural Networks (IJCNN) (Online, 18. July 2021 - 22. July 2021)
DOI: 10.1109/ijcnn52387.2021.9534190
BibTeX: Download
Schwinn L., Raab R., Nguyen A., Zanca D., Eskofier B.:
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
URL: https://arxiv.org/abs/2105.10304
BibTeX: Download

2019

Raab R., Driessens K.:
A generative policy gradient approach for learning to play text-based adventure games
BNAIC/BENELEARN 2019 (Brussels, 6. November 2019 - 8. November 2019)
In: Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019) 2019
Open Access: http://ceur-ws.org/Vol-2491/abstract18.pdf
BibTeX: Download

Year	Student	Type	Title
2024	Jan Šimerda	Master's Thesis	Sharing Medical Trial Data in Federated Gaia-X Data Spaces
2023	Sara Zarifi	Master's Thesis	Lynx Re-Identification from Camera Trap Images in the Wild
2023	Bernhard Schrupp	Master's Thesis	Simulation of Inpatient Claims Data and Fraud Detection from a German Health Insurance Perspective
2023	Jonas Süßkind	Master's Thesis	Video-based Behavior Analysis of Polar Bears Under Human Care
2022	Roman Hucke	Bachelor's Thesis	Analysis of real-time person detection performance on edge devices utilizing state-of-the-art detection methods
2022	Philip Stoll	Master's Thesis	Long-Term Automated Behaviour Monitoring of Captive Polar Bears
2021	Klaus Fischer	Master's Project	Unsupervised Adversarial Representation Learning
2021	Kenneth Mayer	Master's Project	Improving Human Gait Analysis by Super-Resolution and Generative Adversarial Networks
2021	Pavlo Beylin	Master's Thesis	Simultaneous Classification and Identification of Adversarial Attacks Through Multi-Task Learning
2021	Ben Paulsen	Bachelor's Thesis	Prediction of Convective Updrafts in the Atmospheric Boundary Layer Based on Real-World Flight Data: A Machine Learning Approach
2020	Pavlo Beylin	Master's Project	Development of a Modular Adversarial Attack Framework and Evaluation on the Sampled Nonlocal Gradient Descent Method
2019	Katharina Jäger	Master's Thesis	Detecting Labor in Pregnant Women with a Wearable Pregnancy Tracker using Machine Learning

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