Matthias Zürl
Matthias Zürl, M. Sc.
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch-Straße 2b
91058 Erlangen
- Phone number: +49 9131 85-20285
- Email: matthias.zuerl@fau.de
Academic CV
| Since 07/2019 | Researcher at the Machine Learning and Data Analytics Lab Friedrich-Alexander University of Erlangen-Nuremberg |
| Since 07/2019 | Coach of the Innovation Lab for Wearable and Ubiquitous Computing Friedrich-Alexander University of Erlangen-Nuremberg |
| 04/2016 – 12/2018 | Research assistant at the Chair of Applied Physics Friedrich-Alexander University of Erlangen-Nuremberg |
| 10/2016 – 01/2019 | Masters Degree in Physics Friedrich-Alexander-University Erlangen-Nuremberg Master Thesis : “Fabrication and Investigation of Homodyne Detectors for Terahertz Radiation“ |
| 10/2011 – 04/2017 | Study program for High school teaching in Math and Physics Friedrich-Alexander-University Erlangen-Nuremberg |
| 10/2011 – 10/2015 | Bachelors Degree in Math and Physics Friedrich-Alexander-University Erlangen-Nuremberg Bachelor Thesis: “Entwicklung und Aufbau eines Experiments für das physikalische Praktikum für Fortgeschrittene: Transporteigenschaften von Ladungsträgern in Halbleitern – Hall-Effekt” |
Publications
2022
- , , , , , , , , , , , , , :
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
Teaching
Courses
Innovation Lab for Wearable and Ubiquitous Computing
Supervised Projects:
| Term | Title |
| Winter Term 2021 / 22 | VitalSense |
| Summer Term 2021 | Dungineers |
| Winter Term 2020 / 21 | Argus |
| Summer Term 2020 | Stress+ |
| Winter Term 2019 / 20 | Smart City Greens |
Talks
- Tech|Day on Deep Learning, Workshop, Zollhof, Nürnberg, 17.05.2022
- PintOfScience Festival, Vortrag, Nürnberg, 09.05.2022
- Master Digital Buisness Administration, Vorlesungen und Übungen zum Thema Machine Learning, Erlangen, 12.03.2022
- Tech|Day on Deep Learning, Workshop, gemeinsam mit Stefan Seegerer, Zollhof, Nürnberg, 01.12.2020
Material
In the context of several workshops on Deep Learning, which I have conducted together with my colleague Stefan Seegerer, we have developed a cheatsheet for Pytorch, which turned out to be pretty helpful:
Student supervision
| Year | Type | Student | Titel |
| 2022 | Master Thesis | Jonas Beyer |
Unsupervised Polar Bear Re-Identification |
| 2022 | Master Thesis | Philip Stoll |
Long-Term Automated Behaviour Monitoring of Captive Polar Bears |
| 2021 | Master Thesis | Richard Dirauf |
Video-based Re-Identification of captive Polar Bears |
| 2021 | Bachelor Thesis | Ameni Gatri |
Influence of gait test length on classification of mobility impairment in Parkinson’s Disease |
| 2021 | Master Thesis | Wenyu Zhang |
Classification of localized defects on silicon carbide (SiC) wafers using domain adaptation techniques |
| 2020 | Bachelor Thesis | Daniel Seitz |
Unsupervised learning for the classification of process steps in spatial-temporal data |
