Johannes Rabold, M.Sc.

Former Teaching Assistant

M.Sc.

Email:  johannes.rabold(at)uni-bamberg.de

since

10/2017

Research and Teaching Assistant in the Cognitive Systems Group at Otto-Friedrich-Universität Bamberg, Germany

(2021: Winner of the Bavarian Award for Good Teaching ("Preis für Gute Lehre") issued by the state ministry for science and art)

04/2017-

03/2019

Applied Computer Science Study at the Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany (Master of Science)

10/2012-

03/2017

Applied 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
  • Deep Learning
  • Inductive Logic Programming
  • Rabold, J. (2022). A Neural-symbolic Approach for Explanation Generation based on Sub-concept Detection: An Application of Metric Learning for Low-time-budget Labeling. KI - Künstliche Intelligenz. Springer.
  • Rabold, J., Siebers, M., & Schmid, U. (2021). Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach. Machine Learning. Springer.
  • Rabold, J., Schwalbe, G., Schmid, U. (2020). Expressive Explanations of DNNs by Combining Concept Analysis with ILP. 43rd German Conference on Artificial Intelligence.
  • Rabold, J., Deininger, H., Siebers, M., & Schmid, U. (2019). Enriching Visual with Verbal Explanations for Relational Concepts – Combining LIME with Aleph. AIMLAI-XKDD (ECML PKDD Workshop).
  • Schallner, L., Rabold, J., Scholz, O., & Schmid, U. (2019). Effect of Superpixel Aggregation on Explanations in LIME--A Case Study with Biological Data.AIMLAI-XKDD (ECML PKDD Workshop).

  • 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.

  • Rabold, J., Siebers, M., & Schmid, U. (2018, September). Explaining black-box classifiers with ILP – empowering LIME with Aleph to approximate non-linear decisions with relational rules. In International Conference on Inductive Logic Programming (pp. 105-117). Springer, Cham.

Selected Activities

  • Elected deputy of the non-professorial teaching staff of the Faculty of Intelligent Systems and Applied Computer Sciences
  • Elected member of the student's association of the Faculty of Intelligent Systems and Applied Computer Sciences (during study)

Conference Organization

  • Chair for the StudentDay@KI2020

Given Talks (non-conference)

  • Invited Talk to RuleML: Enriching Visual with Verbal Explanations for Relational Concepts (2020)

Given Workshops

  • Workshop 'Machine Learning and Data Science - Hands-On with KNIME' during the 'Woche der Forschung' (2 times)
  • TAO pupil workshop 'Code your own AI'

Participation in Seminars

  • Invited participant at the Dagstuhl Seminar on Approaches and Applications of Inductive Programming