Thomas Altstidl

Thomas Altstidl, M. Sc.

Researcher & PhD Candidate

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

Room: Room 01.012
Carl-Thiersch-Straße 2b
91052 Erlangen

Microsoft Teams

📷 ul95odob@fauad.fau.de

Academic CV

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”

Research Projects

Publications

2021

Students

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
06/2022 – 11/2022 Christopher Kraus (Master Thesis)
Compression of spatial information in wideband channel measurements using generative models