Leo Schwinn
Leo Schwinn, M. Sc.
Alumnus
Carl-Thiersch-Straße 2b
91052 Erlangen
Germany
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch-Straße 2b
91052 Erlangen
Germany
- Phone number: +49 9131 85-20296
- Email: leo.schwinn@fau.de
- Website: https://www.mad.tf.fau.de/person/leo-schwinn/
Academic CV
| 02/2022 – 05/2022 | Internship
Supervised by Doina Precup |
| 09/2019 – 11/2022 | Researcher and PhD Candidate
Machine Learning and Data Analytics Lab, |
| 01/2019-03/2019 | Scientific Assistant
IT Security Infrastructures Lab (CS1), |
| 10/2017-08/2019 | M.Sc. in Medical Engineering, “Medical Image and Data Processing”
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Pattern Recognition, Machine Learning, Deep Learning |
| 10/2014-10/2017 | B.Sc. in Medical Engineering, “Medical Imaging Systems”
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Pattern Recognition |
Research Projects
Teaching
Publications
2022
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xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning
In: Sensors 22 (2022), Article No.: 8474
ISSN: 1424-8220
DOI: 10.3390/s22218474
BibTeX: Download - , , , , , , , :
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
2021
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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 - , , , , , , , , , , :
System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data
In: IEEE Access (2021)
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3106791
BibTeX: Download - , , , , , , , :
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 - , , , , , , :
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
International Joint Conference on Neural Networks (IJCNN) (Online, 18. July 2021 - 22. July 2021)
BibTeX: Download - , , , , :
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
URL: https://arxiv.org/abs/2105.10304
BibTeX: Download
2020
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Time matters: Time-aware LSTMs for predictive business process monitoring
International Conference on Process Mining (Padua, 4. October 2020 - 9. October 2020)
In: Proceedings of the ICPM 2020 International Workshops 2020
BibTeX: Download
2019
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Impact of Pathologies on Automatic Age Estimation
Fortschritte der Akustik - DAGA 2019 (DAGA 2019 - 45. Jahrestagung für Akustik) (Rostock, 18. March 2019 - 21. March 2019)
In: Deutsche Gesellschaft für Akustik e.V. (ed.): Fortschritte der Akustik - DAGA 2019, Rostock: 2019
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2019/Schwinn19-IOP.pdf
BibTeX: Download
Student Theses
| Year | Name | Title |
| 2022 | Tim Löhr | AI Trend Detection in Healthcare by applying Topic Clustering and Sentiment Analysis using Podcast Data (Master’s Thesis, Co-Supervision) |
| 2022 | Andrey Kurzyukov | Modeling Mixed-Type Time Series Data for Machinery Health Prognostics (Master’s Thesis, Co-Supervision) |
| 2021 | Klaus Fischer | Unsupervised Adversarial Representation Learning (Master’s Project) |
| 2021 | Kenneth Mayer | Improving Human Gait Analysis by Super-Resolution and Generative Adversarial Networks (Master’s Project, Co-Supervision) |
| 2021 | Jonas Utz |
Unsupervised Modeling of Visual Attention (Master’s Project, Co-Supervision) |
| 2021 | Meike Biendl | Exploring Connections between Visual Attention and Adversarial Robustness (Master’s Thesis) |
| 2021 | Pavlo Beylin | Simultaneous Classification and Identification of Adversarial Attacks Through Multi-Task Learning (Master’s Thesis) |
| 2021 | Johannes Roider |
Modeling Mixed-Type Time Series Data With Neural Networks for Predictive Maintenance (Master’s Thesis, Co-Supervision) |
| 2021 | Tassilo Elsberger | Analysis of Artificial Intelligence Trends in German Economics and Politics – A Data-Driven Approach (Master’s Thesis, Co-Supervision) |
| 2021 | Miaomiao Li | Adaptive Lipschitz Regularization of Neural Networks through Matrix Decomposition (Master’s Thesis) |
| 2021 | Long Do | Artificial Intelligence trend analysis using speech-to-text data from healthcare podcasts (Master’s Thesis, Co-Supervision) |
| 2020 | Pavlo Beylin | Development of a Modular Adversarial Attack Framework and Evaluation on the Sampled Nonlocal Gradient Descent Method (Master’s Project) |
| 2020 | Osman Demir | group:Anomaly Semantic Segmentation with Learnable Wavelet Filters (Master’s Thesis) |
| 2020 | Darko Boshkovski | group:Using speech recognition techniques for the application of online handwriting recognition (Bachelor’s Thesis, Co-Supervision) |