René Raab

Chair of Machine Learning and Data Analytics

Doctoral candidates

Address

Carl-Thiersch-Straße 2b91052 Erlangen

Room: 01.020, Floor: 01

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.

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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