Software Methodologies for Distributed Systems

  1. University
  2. Faculties
  3. Faculty of Applied Computer Science
  4. Institute of Computer Science
  5. Chairs and Professors
  6. Software Methodologies for Distributed Systems
  7. Team
  8. Fabian Stieler

Fabian Stieler M.Sc.

Research Assistant
Software Methodologies for Distributed Systems
Phone: +49 821 598-2471
Email:
Room: 3005 (N)
Open hours: by arrangement
Address: Universitätsstraße 6a, 86159 Augsburg

Short Curriculum Vitae

  • 2017: Bachelor of Science, Wirtschaftsinformatik
  • 2019: Master of Science, Informatik & Informationswirtschaft

Fields of Research

  • Machine Learning Operations (MLOps)
  • Explainable Artificial Intelligence (XAI)
  • Active Learning (AL)

Mentored Theses

  • Reduction of Annotation Effort - A Comparison of Active Learning Frameworks (Bachelorthesis), Original Title: "Reduktion des Annotationsaufwands - Ein Vergleich von Active Learning Frameworks"
  • Bias Detection using Interpretable Machine Learning Methods (Bachelorthesis)
  • Evaluating Explainability in the Context of Active Learning (Masterthesis)
  • Interpretable Machine Learning with ECG Data (Bachelorthesis), Original Title: "Interpretierbares Maschinelles Lernen mit EKG Daten"
  • Model Explanations as Quality Gate in Machine Learning Pipelines (Masterthesis)

Projects

Publications

2023

Fabian Stieler and Bernhard Bauer. 2023. Git workflow for active learning - a development methodology proposal for data-centric AI projects. In Hermann Kaindl, Mike Mannion and Leszek Maciaszek (Ed.). Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2023), April 24-25, 2023, in Prague, Czech Republic. SciTePress, Setúbal, 202-213 DOI: 10.5220/0011988400003464
PDF | BibTeX | RIS | DOI

 

Miriam Elia, Tobias Peter, Fabian Stieler, Bernhard Bauer, Sandra Nagl, Alanna Ebigbo and Vivien Grünherz. in press. Precision medicine for achalasia diagnosis: a multi-modal and interdisciplinary approach for training data generation [Abstract]. In IEEE - ISBI 2023: International Symposium on Biomedical Imaging, Cartagena de Indias, Colombia, April 18-21, 2023. IEEE, Piscataway, NJ
PDF | BibTeX | RIS | URL

 

2022

Fabian Stieler, Marius Nann and Bernhard Bauer. 2022. Vertrauenswürdige KI in der Medizin - von effizienter Datenannotation bis intuitiver Modellerklärung. In KI-Fabrigk @WIKOIN22, Ingolstadt, 30. Juni 2022.
BibTeX | RIS

 

2021

Fabian Stieler, Fabian Rabe and Bernhard Bauer. 2021. Towards domain-specific explainable AI: model interpretation of a skin image classifier using a human approach. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021, 19-25 June 2021, Nashville, TN, USA. IEEE, Piscataway, NJ, 1802-1809 DOI: 10.1109/CVPRW53098.2021.00199
PDF | BibTeX | RIS | DOI

 

2020

Fabian Stieler, Fabian Rabe and Bernhard Bauer. 2020. Federated medical data - how much can deep learning models benefit? [Poster]. In AMIA 2020 Virtual Clinical Informatics Converence, May 19-21.
PDF | BibTeX | RIS

 

2018

Harald Schmidbauer, Angi Rösch and Fabian Stieler. 2018. Patterns of interest in industrial brands: Instagram media uploads and sentiment. In Len Tashman and Fotios Petropoulos (Ed.). International Symposium on Forecasting (ISF) 2018, Boulder (CO), USA, June 16-20, 2018. International Institute of Forecasters
PDF | BibTeX | RIS | URL

 

 

Harald Schmidbauer, Angi Rösch and Fabian Stieler. 2018. The 2016 US presidential election and media on Instagram: who was in the lead?. Computers in Human Behavior 81, 148-160. DOI: 10.1016/j.chb.2017.11.021
PDF | BibTeX | RIS | DOI

 

2017

Harald Schmidbauer, Angi Rösch and Fabian Stieler. 2017. Patterns of interest in industrial brands: media uploads to Instagram. In 23rd IIF Workshop on Predictive Analytics and Forecasting (IIF 2017) - Research and Applications, Munich, Germany, September 14-15, 2017. International Institute of Forecasters
BibTeX | RIS

Search