Bettina Finzel

Research Associate (Doctoral Candidate)

 

Office: WE5/04.028
Office hours: Thu, 10:00 - 12:00, by apppointment otherwise

Phone: +49 951 863 2878
Email: bettina.finzel(at)uni-bamberg.de

orcid.org/0000-0002-9415-6254

 

Bettina Finzel holds a Master degree in Applied Computer Science from the University of Bamberg. In 2018 she joined the Cognitive Systems Group as a research associate and doctoral candidate in the project TraMeExCo, which is founded by the Federal Ministry for Education and Research.

since 10/2018Research Associate in the project Transparent Medical Expert Companion (TraMeExCo) founded by BMBF at Cognitive Systems Group, Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany
10/2016 - 09/2019Applied Computer Science Study at the Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany (Master of Science)
04/2018 - 09/2018Internship concerning the Implementation of Predictive Analytics at MHP Management- und IT-Beratung GmbH
10/2012 - 03/2017Applied Computer Science Study at the Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany (Bachelor of Science)

 

Main Research Interests

  • Explainable AI and Interactive Machine Learning for the Medical Domain
  • Constraint Programming for Relational Learning, especially Mutual Explanations
  • Mentoring in Computer Science
  • Rieger, Finzel, Seuß, Wittenberg, Schmid (2019) Make Pain Estimation Transparent: A Roadmap to Fuse Bayesian Deep Learning and Inductive Logic Programming (Poster). 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC, 23.-27. Juli 2019, Berlin).
  • Finzel B., Schmid U. (2019) Erklärbare KI für medizinische Anwendungen (Talk). 49. Kongress der Deutschen Gesellschaft für Endoskopie und Bildgebende Verfahren e.V. (28.-30. März 2019, Stuttgart), Track der Deutschen Gesellschaft für Biomedizinische Technik "Krankenhaus der Zukunft".
  • Finzel B., Rabold J., Schmid U. (2019) Explaining Relational Concepts: When Visualisation and Visual Interpretation of a Deep Neural Network's Decision are not enough (Abstract). Europäische Konferenz zur Datenanalyse (ECDA, 18.-20. März 2019, Bayreuth), Special Session on Interpretable Machine Learning.
  • Finzel B., Deininger H., Schmid U. (2018) From beliefs to intention: mentoring as an approach to motivate female high school students to enrol in computer science studies. In Proceedings of the 4th Conference on Gender & IT (GenderIT '18). ACM, New York, NY, USA, 251-260.
  • Elmamooz, G., Finzel, B. & Nicklas, D., (2017). Towards Understanding Mobility in Museums. In: Mitschang, B., Nicklas, D., Leymann, F., Schöning, H., Herschel, M., Teubner, J., Härder, T., Kopp, O. & Wieland, M. (Hrsg.), Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband. Bonn: Gesellschaft für Informatik e.V.. (S. 127-136).

All Publications

Awards

  • Award of the Ministry for Environment and Health (2012) for a Sustainability and Nutrition Education Project for Elementary Schools
  • Female Tech Talents Stipendium (2018) awarded by Campusjäger

Selected Activities

  • Member of the German Informatics Society
  • Alumni Advisor for the make IT Computer Science Mentoring at the University of Bamberg (voluntary)
  • Elected member of the Czech-German Discussion Forum, participation in the task force for social and economic affairs, especially digital transformation topics (voluntary)
  • Workshops
    • Das Mentoring Program make IT, 16. Arbeitstagung der Konferenz der Einrichtungen für Frauen- und Geschlechterstudien im deutschsprachigen Raum (KEG), Arbeitsgruppe „Mentoringprojekte in der Informatik“, University of Music and Performing Arts, Vienna, 26.9.2018 -- [Program]
    • Shiny R - Analysen interaktiv gestalten und visualisieren, course at the TAO-SFZ-Workshop „KI selber programmieren“, University of Bamberg 29.-31.10.2018 -- [Report]
    • Maschinelles Lernen und Data Science – Hands-On mit KNIME, Woche der Forschung, University of Bamberg, 26.2.2019 -- [Program]
  • Lectures
    • Classifying Medical Data with Neural Nets and Inductive Logic Programming, lecture including practical tasks in R and Prolog, University of Economics Prague, 22.10.2019 -- [Event]
  • Talks
    • Gedanken zu Künstlicher Intelligenz und der künftigen Gesellschaft, Diskussionspanel Gesellschaft 4.0, Jahreskonferenz des Deutsch-Tschechischen Gesprächsforums zum Thema "Die Zukunft der deutsch-tschechischen Beziehungen aus der Sicht junger Deutscher und Tschechen", Olomouc, 20.11.2018 -- [Program]
    • Künstliche Intelligenz und Medizin: Von operierenden Robotern und Computerdiagnosen, Tag der Begabtenförderung 2019, Kloster Banz, 8.7.2019 -- [Report]
    • Learning Expressive First Order Rules - Introduction to Inductive Logic Programming, talk held in KIZI seminar, University of Economics Prague, 22.10.2019 -- [Event]
    • Skalpell bitte! KI in der Medizin: Chancen und Herausforderungen, KI und Wir* Convention, Magdeburg, 23.11.2019 -- [Program], [Abstract]

Participation in Schools

  • Interdisciplinary College 2019 Günne/Möhnesee Germany “Out of your senses: from data to insight”, intense one-week spring school, 12.-18.3.2019
  • ECML PKDD Summer School 2019 Würzburg Germany “Machine Learning and Data Mining for Geo-Spatial Data/Volunteered Geographic Information, Quality of Experience and Human-Computer Interaction (EPSS19)”, intense one-week spring school, 11.-16.10.2019
    • included paper preparation in group work for submission at an international conference