René Raab

René Raab, M. Sc.

Researcher & PhD Candidate

Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik

Room 01.020
Carl-Thiersch-Straße 2b
91052 Erlangen

since 09/2019	Researcher and 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
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

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)
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

Year	Student	Type	Title
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