Felix Haag

• 2020 - today: Scientific assistant and PhD student at the Energy Efficient Systems Group, Otto-Friedrich University of Bamberg
• 2018 - 2021: Information Systems (M.Sc.), Otto-Friedrich-University of Bamberg – Degree with distinction
• 2017: Internship at Mercedes-Benz Malaysia in Kuala Lumpur
• 2015 - 2018: Information Systems (B.Sc.), Baden-Württemberg Cooperative State University in cooperation with Mercedes-Benz Tech Innovation

• (Explainable) Machine Learning
• Learning Analytics
• Smart Meter Data Analytics
• Information Systems and Applications of Energy Informatics

Günther, S. A., Haag, F., Hopf, K., Klose, M., Handschuh, P., Staake, T. (forthcoming): A feedback component that leverages counterfactual explanations for smart learning support: First insights into its empirical evaluation. DiKuLe Symposium 2022 (forthcoming)

Haag, F., Hopf, K., Menelau Vasconcelos, P., Staake, T. (2022): Augmented Cross-Selling Through Explainable AI – A Case From Energy Retailing. European Conference on Information Systems (ECIS) 2022, Timișoara, Romania [link]

Wastensteiner, J., Weiss, T., Haag, F., Hopf, K. (2021): Explainable AI for Tailored Electricity Consumption Feedback – an Experimental Evaluation of Visualizations. European Conference on Information Systems (ECIS) 2021, Marrakech, Morocco [link]

Haag, F. (2022): Explainable Machine Learning to Augment Human Decision-Making. Doctoral Consortium during the 30th European Conference on Information Systems (ECIS), Timișoara, Romania, June 19

Hopf, K., Haag, F. (2020): Explainable AI for Enhanced Human-AI Interaction. Pre-ICIS Practice Development Workshop “AI Beyond the Hype”, Online, December 13