Leo Schwinn | MaD Lab

Dr. Leo Schwinn

Alumnus

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

Room 01.016
Carl-Thiersch-Straße 2b
91052 Erlangen
Germany

Email: leo.schwinn@fau.de
Website: https://www.mad.tf.fau.de/person/leo-schwinn/

Academic CV

01/2023-11/2023	Postdoc Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
01/2022-01/2023	Research Stay Mila – Quebec AI Institute Supervised by Doina Precup (Partially conducted online due to the pandemic)
09/2019-11/2022	PhD Candidate Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
01/2019-03/2019	Scientific Assistant IT Security Infrastructures Lab (CS1), Department of Computer Science, Friedrich-Alexander-Universität (FAU)
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 Master’s Thesis: “Learning Feature Extraction for End-to-End Speaker Recognition with Deep Neural Networks”
10/2014-10/2017	B.Sc. in Medical Engineering, “Medical Imaging Systems” Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Pattern Recognition Bachelor’s Thesis: “Unterscheidung von Sprachpathologien durch Sprechererkennungsverfahren”

Research Projects

SHIELD

Teaching

MLTS Excercise

Publications

2024

Owsienko D., Schwinn L., Eskofier B., Kiesswetter E., Loos H.:
Sensory evaluation of axillary odour samples of younger and older adults by a trained panel
In: Flavour and Fragrance Journal 39 (2024), p. 3-9
ISSN: 0882-5734
DOI: 10.1002/ffj.3762
BibTeX: Download

2023

Altstidl TR., Nguyen AT., Schwinn L., Köferl F., Mutschler C., Eskofier B., Zanca D.:
Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
2023 International Joint Conference on Neural Networks (IJCNN) (Gold Coast, Australia, 18. June 2023 - 23. June 2023)
In: Proc. Intl. Joint Conf. Neural Netw. (IJCNN) 2023
DOI: 10.1109/IJCNN54540.2023.10191724
BibTeX: Download
Dumbach P., Schwinn L., Löhr T., Do PL., Eskofier B.:
Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach
In: Evolutionary Intelligence (2023)
ISSN: 1864-5909
DOI: 10.1007/s12065-023-00878-4
URL: https://link.springer.com/article/10.1007/s12065-023-00878-4
BibTeX: Download
Dumbach P., Schwinn L., Löhr T., Elsberger T., Eskofier B.:
Artificial intelligence trend analysis in German business and politics: a web mining approach
In: International Journal of Data Science and Analytics (2023), p. 1-22
ISSN: 2364-415X
DOI: 10.1007/s41060-023-00483-9
URL: https://link.springer.com/article/10.1007/s41060-023-00483-9
BibTeX: Download
Härtl T., Owsienko D., Schwinn L., Hirsch C., Eskofier B., Lang R., Wirtz S., Loos H.:
Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study
In: Frontiers in Ecology and Evolution 11 (2023), Article No.: 1107463
ISSN: 2296-701X
DOI: 10.3389/fevo.2023.1107463
BibTeX: Download
Klede K., Schwinn L., Zanca D., Eskofier B.:
FastAMI - A Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
AAAI 2023 (Washington, D.C., 7. February 2023 - 14. February 2023)
In: Proceedings of the AAAI Conference on Artificial Intelligence, 37(7) 2023
DOI: 10.1609/aaai.v37i7.26003
URL: https://ojs.aaai.org/index.php/AAAI/article/view/26003
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

Link J., Schwinn L., Pulsmeyer F., Kautz T., Eskofier B.:
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
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

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
Nguyen A., Foerstel S., Kittler T., Kurzyukov A., Schwinn L., Zanca D., Hipp T., Jun SD., Schrapp M., Rothgang E., Eskofier B.:
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
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

2020

Nguyen A., Chatterjee S., Weinzierl S., Schwinn L., Matzner M., Eskofier B.:
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
DOI: 10.1007/978-3-030-72693-5_9
BibTeX: Download

2019

Schwinn L., Haderlein T., Nöth E., Maier A.:
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
2023	Fabio Rosenthal	Adaptive Resolution Transformers for High-Resolution Image Processing (Master’s Thesis)
2022	Fabio Rosenthal	Unsupervised Modelling of Free-Viewing Human Scanptahs with Biologically Constrained RNNs (Master’s Project)
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	Anomaly Semantic Segmentation with Learnable Wavelet Filters (Master’s Thesis)
2020	Darko Boshkovski	Using speech recognition techniques for the application of online handwriting recognition (Bachelor’s Thesis, Co-Supervision)