Oelke, Daniela
Prof. Dr. rer. nat.
Machine Learning, Visual Analytics, Natural Language Processing, Erklärbare Künstliche Intelligenz
- 0781 205-4894
- daniela.oelke@hs-offenburg.de
- Raum: C114
- Badstraße 24, 77652 Offenburg
Funktion
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Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI), Professor*in
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Fakultätsrat EMI, Mitglied
Lehrveranstaltungen (aktuelles und vorhergehendes Semester)
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Seminar 1, EMI929
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Praktikum Data Engineering und ML Operations, EMI928
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Präsentationstechnik, EMI919
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Natural Language Processing, EMI925
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Betriebspraktikum, EMI930
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Kommunikation und Interaktion in Unternehmen, EMI918
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Machine Learning 1, EMI908
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Praktikum Natural Language Processing, EMI926
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Kolloquium, EMI940
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Grundlagen der Informatik, EMI903
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Machine Learning 2, EMI914
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Projekt 1, EMI920
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IT-Recht, EMI140
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Praktikum Machine Learning 1, EMI909
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Data Engineering und ML Operations, EMI927
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Projekt 2, EMI933
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Bachelor-Thesis, EMI939
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Projektmanagement, EMI913
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Kolloquium Betriebliche Praxis, EMI931
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Visual Analytics, EMI906
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Praktikum Visual Analytics, EMI907
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Praktikum Machine Learning 2, EMI915
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Seminar 2, EMI938
Publikationen
2024 | |||
| Stefan Glaser, Thomas Seifert, Daniela Oelke | Can Machine Learning and Explainable Artificial Intelligence Help to Improve an Expert Model for Predicting Thermomechanical Fatigue? | Proceedings of the Upper-Rhine Artificial Intelligence Symposium 2024, Janis Keuper, Klaus Dorer (Eds.) pp. 21-30, 2024 | BibTeX | RIS DOI |
| Xiao Zhao, Marko Weber, Jan Schöffmann, Daniela Oelke | Digitalisierung von Raster-Zeichnungen mit Deep Learning ; Wie ein Framework OCR-Software bei der Extraktion von Daten übertrifft | Industry 4.0 Science, 40(6), pp. 10-17, 2024 | BibTeX | RIS |
| Thomas Meyer, Robert Meyer, Daniela Oelke, Ralph Wäsch, Justus Duyster, Jürgen Finke, Robert Zeiser, Claudia Wehr | Machine Learning Based Prediction of One Year Mortality after Allogeneic Hematopoietic Cell Transplantation (alloHCT) Highlights Importance of Pre-Transplant Immunocompetence | Blood : 66th ASH Annual Meeting Abstracts, pp. 106-107, 2024 | BibTeX | RIS DOI |
2023 | |||
| Polo Chau, Alex Endert, Daniel A. Keim, Daniela Oelke | Interactive Visualization for Fostering Trust in ML | Dagstuhl Reports, pp. 103-116, 2023 | BibTeX | RIS DOI |
| Udo Schlegel, Daniela Oelke, Daniel A. Keim, Mennatallah El-Assady | Preprint: Visual Explanations with Attributions and Counterfactuals on Time Series Classification | pp. 1-14, 2023 | BibTeX | RIS DOI ARXIV |
2021 | |||
| Udo Schlegel, Daniela Oelke, Daniel A. Keim, Mennatallah El-Assady | An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks | [NeurIPS 2020 Workshops], pp. 1-7, 2021 | BibTeX | RIS ARXIV |
| Daniela Oelke, Daniel A. Keim, Polo Chau, Alex Endert | Interactive Visualization for Fostering Trust in AI | Dagstuhl Reports, pp. 37-42, 2021 | BibTeX | RIS DOI |
| Emma Beauxis-Aussalet, Michael Behrisch, Rita Borgo, Duen Horng Chau, Christopher Collins, Daniela Oelke, David Ebert, Mennatallah El-Assady, Alex Endert, Daniel A. Keim, Jörn Kohlhammer, Jaakko Peltonen, Maria Riveiro, Tobias Schreck, Hendrik Strobelt, Jarke J. van Wijk | The Role of Interactive Visualization in Fostering Trust in AI | IEEE Computer Graphics and Applications, 41(6), pp. 7-12, 2021 | BibTeX | RIS DOI |