Miriam Elia M.Sc.
Telefon: | +49 821 598 2174 |
E-Mail: | miriam.elia@informatik.uni-augsburg.de |
Raum: | 3004 (N) |
Adresse: | Universitätsstraße 6a, 86159 Augsburg |
Kurzlebenslauf
- Seit Dezember 2021: Wissenschaftliche Mitarbeiterin, Professur Softwaremethodik für verteilte Systeme, Universität Augsburg
- 2021: Master of Science (Wirtschaftsinformatik), Universität Augsburg
- 2019: Bachelor of Science (Wirtschaftsinformatik), Universität Augsburg
- 2016: Bachelor of Education (Englisch & Französisch), Universität Augsburg
Forschungsbereiche
- Trustworthy AI
- Machine Learning in Healthcare
- Certifiable AI in Medicine
- Creation of a Generic and Customizable Methodology based on Quality Gates towards Certifiable AI in Medicine
Projekte
Abschlussarbeiten
- "Zusammenführung multimodaler Datenquellen in einer Anwendung zur Analyse des Ösophagus für die Diagnoseunterstützung von Achalasie" - Masterarbeit
Publikationen
2023 |
Miriam Elia and Bernhard Bauer. in press. A methodology based on quality gates for certifiable AI in medicine: towards a reliable application of metrics in machine learning. In ICSOFT 2023: 18th International Conference on Software Technologies, 10-12 July 2023, Rome, Italy. SciTePress, Setúbal |
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 |
Dominik Mueller, Silvan Mertes, Niklas Schroeter, Fabio Hellmann, Miriam Elia, Bernhard Bauer, Wolfgang Reif, Elisabeth André and Frank Kramer. 2023. Towards automated COVID-19 presence and severity classification. In Maria Hägglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindsköld and Parisis Gallos (Ed.). Caring is sharing – exploiting the value in data for health and innovation. IOS Press, Amsterdam (Studies in Health Technology and Informatics ; 302), 917-921. DOI: 10.3233/shti230309 |
2021 |
Miriam Elia, Carola Gajek, Alexander Schiendorfer and Wolfgang Reif. 2021. An interactive web application for decision tree learning. In Bernd Bischl, Oliver Guhr, Heidi Seibold and Peter Steinbach (Ed.). Proceedings of the Teaching Machine Learning Workshop at ECML-PKDD 2020, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 14 September 2020, Virtual Conference. ML Research Press (PMLR - Proceedings of Machine Learning Research ; 141), 11-16. |