Thomas Altstidl

Thomas Altstidl, M. Sc.

Researcher & PhD Candidate

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

Room 01.012
Carl-Thiersch-Straße 2b
91052 Erlangen

Phone number: +49 9131 85-27921
Email: thomas.r.altstidl@fau.de
Website: https://www.mad.tf.fau.de/person/thomas-altstidl/

Since 04/2021	Ph.D. Student Machine Learning and Data Analytics Lab at Friedrich-Alexander-University Erlangen-Nürnberg
04/2020 – 06/2020	Student Research Assistant Friedrich-Alexander-University Erlangen-Nürnberg, Machine Learning and Data Analytics Lab
10/2018 – 03/2021	M.Sc. in Computer Science Friedrich-Alexander-University Erlangen-Nürnberg Master Thesis: “Scale-invariant Convolution Kernels”
04/2019 – 03/2020 & 11/2020 – 01/2021	Student Research Assistant Fraunhofer Society, Precise Positioning and Analytics Department
04/2017 – 08/2017 & 10/2017 – 02/2018	Student Teaching Assistant Friedrich-Alexander-University Erlangen-Nürnberg, Distributed Systems and Operating Systems Group
10/2015 – 04/2019	B.Sc. in Computer Science Friedrich-Alexander-University Erlangen-Nürnberg Bachelor Thesis: “Application of Deep Learning Methods to Process Natural Phenomena”

DigiOnko

2024

Dietz S., Altstidl TR., Zanca D., Eskofier B., Nguyen A.:
How Intermodal Interaction Affects the Performance of Deep Multimodal Fusion for Mixed-Type Time Series
International Joint Conference on Neural Networks (IJCNN) (Yokohama, 30. June 2024 - 5. July 2024)
DOI: 10.1109/IJCNN60899.2024.10650421
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10650421
BibTeX: Download

2023

Altstidl TR., Nguyen A., 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
Gabler E., Nissen M., Altstidl TR., Titzmann A., Packhäuser K., Maier A., Fasching P., Eskofier B., Leutheuser H.:
Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning
45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 (Sydney, NSW, 24. July 2023 - 27. July 2023)
In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2023
DOI: 10.1109/EMBC40787.2023.10340336
BibTeX: Download
Klede K., Altstidl TR., Zanca D., Eskofier B.:
p-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison
Thirty-seventh Annual Conference on Neural Information Processing Systems (New Orleans, 10. December 2023 - 16. December 2023)
In: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine (ed.): Advances in Neural Information Processing Systems 36 2023
Open Access: https://proceedings.neurips.cc/paper_files/paper/2023/file/563d94819f68cb73d6a382809e587b54-Paper-Conference.pdf
URL: https://proceedings.neurips.cc/paper_files/paper/2023/file/563d94819f68cb73d6a382809e587b54-Paper-Conference.pdf
BibTeX: Download

2022

Hecht D., Pfahler T., Ullmann I., Altstidl TR., Amer N., Jin Y., Eskofier B., Vossiek M.:
In Vivo Skin-Type Classification Using Millimeter-Wave Near-Field Probe Spectroscopy
2022 52nd European Microwave Conference (Milan, 27. September 2022 - 29. September 2022)
In: Institute of Electrical and Electronics Engineers (IEEE) (ed.): 2022 52nd European Microwave Conference, Milan: 2022
DOI: 10.23919/EuMC54642.2022.9924376
BibTeX: Download

2021

Altstidl TR., Kram S., Herrmann O., Stahlke M., Feigl T., Mutschler C.:
Accuracy-Aware Compression of Channel Impulse Responses using Deep Learning
International Conference on Indoor Positioning and Indoor Navigation (IPIN2021) (Lloret de Mar, 29. November 2021 - 2. December 2021)
In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021) 2021
DOI: 10.1109/IPIN51156.2021.9662545
BibTeX: Download

WS 2023/24	Machine Learning for Engineers I Machine Learning for Engineers II
SS 2023	Machine Learning for Engineers I Machine Learning for Engineers II
WS 2022/23	Machine Learning for Engineers I Machine Learning for Engineers II
SS 2022	Machine Learning for Engineers I Machine Learning for Engineers II
WS 2021/2022	Machine Learning for Engineers I Machine Learning for Engineers II
SS 2021	Machine Learning for Engineers I Machine Learning for Engineers II

07/2021 – 01/2022	Simon Dietz (Master Thesis) Machine Learning Methods for Mixed-Type Time Series Analysis (main supervisor An Nguyen)
12/2021 – 04/2022	Juliane Hoffmann (Bachelor Thesis) Annotation-efficient learning of surgical instrument activity
01/2022 – 06/2022	Stefan Kraft (Master Thesis) Self-supervised Learning of Monocular Depth Estimation in Crane Environments
01/2022 – 06/2022	Sven Steinkemper (Master Thesis) Machine Learning for Systems Biology: Data Analysis of IBD Patients’ Microarray Data
05/2022 – 10/2022	Elisabeth Gabler (Bachelor Thesis) Fetal Re-Identification: Deep Learning on Pregnancy Ultrasound Images (main supervisor Michael Nissen)
06/2022 – 11/2022	Christopher Kraus (Master Thesis) Compression of spatial information in wideband channel measurements using generative models
10/2022 – 04/2023	Martin Zeus (Master Thesis) Predicting Disease Progression in Multiple Sclerosis using Machine Learning
03/2023 – 09/2023	Nhat Anh Phung Tuan (Master Thesis) Evaluation of NeVA in Predicting Radiologist’s Eye Movement on Chest X-ray Data (main supervisor Dario Zanca)
06/2023 – 12/2023	Maximilian Gaul (Master Thesis) Comparison of different radio modules for CSI sensing and positioning (with Maximilian Stahlke)
06/2023 – 12/2023	Leon Brasseler (Master Thesis) None-Line-of-Sight Detection for Radio Localisation using Deep State Space Models (with Maximilian Stahlke)

Thomas Altstidl

Thomas Altstidl, M. Sc.

Academic CV

Research Projects

Publications

2024

2023

2022

2021

Teaching

Students