Johannes is a research associate at the Applied AI in Services Lab within the Institute of Information Systems and Marketing (IISM) and the Karlsruhe Service Research Institute (KSRI). His interests include:

  •  Deep learning given limited data
  •  Foundation models
  •  Data-centric AI
  •  Few-shot learning
  •  Human-centered artificial intelligence 

Please feel free to reach out if you are interested in writing a bachelor's or master's thesis on one of these topics.

Curriculum Vitae

Johannes graduated from KIT with a bachelor’s and a master’s degree with a focus on machine learning, statistics, and optimization. During his master’s degree, he studied Information Systems and Computer Engineering at the Instituto Superior Técnico in Lisbon, Portugal. He authored his thesis at ETH Zurich at the Chair of Management Information Systems. Throughout his master's studies, Johannes worked on real-world machine learning implementations (e.g., computer vision, speech recognition, named entity recognition) during internships at Porsche Motorsport, d-fine, and EnBW AG. At the beginning of his Ph.D. program, he worked as an Affiliated Research Member at ETH Zurich, where he researched emotion dynamics on social media. In October 2022, Johannes joined IBM Research as a Visiting Researcher and since then has been working on geospatial foundation models and the segmentation of natural hazards on satellite images. Johannes is part of a research collaboration between IBM Research and NASA, where he is responsible for fine-tuning geospatial foundation models to downstream applications.


Book Chapters
An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making
Jakubik, J.; Schöffer, J.; Hoge, V.; Vössing, M.; Kühl, N.
2023. Machine Learning and Principles and Practice of Knowledge Discovery in Databases – International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part I. Ed.: I. Koprinska, 353–368, Springer Nature Switzerland AG. doi:10.1007/978-3-031-23618-1_24
Conference Papers
Learning to Defer with Limited Expert Predictions
Hemmer, P.; Thede, L.; Vössing, M.; Jakubik, J.; Kühl, N.
2023. Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, February 7-14, 2023
Journal Articles
Incorporating financial news for forecasting Bitcoin prices based on long short-term memory networks
Jakubik, J.; Nazemi, A.; Geyer-Schulz, A.; Fabozzi, F. J.
2022. Quantitative Finance, 23 (2), 335–349. doi:10.1080/14697688.2022.2130085
Data‐driven allocation of development aid towards Sustainable Development Goals: Evidence from HIV/AIDS
Jakubik, J.; Feuerriegel, S.
2022. Production and Operations Management, 31 (6), 2739–2756. doi:10.1111/poms.13714
Conference Papers
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
Hemmer, P.; Schellhammer, S.; Vössing, M.; Jakubik, J.; Satzger, G.
2022. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2478–2484, International Joint Conferences on Artificial Intelligence Organization (IJCAI). doi:10.24963/ijcai.2022/344
An Empirical Evaluation of Estimated Outcomes as Explanations in Human-AI Decision-Making
Jakubik, J.; Schöffer, J.; Hoge, V.; Vössing, M.; Kühl, N.
2022. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning
Jakubik, J.; Blumenstiel, B.; Vössing, M.; Hemmer, P.
2022. Wirtschaftsinformatik 2022 : Proceedings. Bd.: 6, AIS eLibrary (AISeL)
Designing a Human-in-the-Loop System for Object Detection in Floor Plans
Jakubik, J.; Hemmer, P.; Vössing, M.; Blumenstiel, B.; Bartos, A.; Mohr, K.
2022. Proceedings of the 36th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI 2022), Online, 22.02.2022-01.03.2022, 12524–12530, Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v36i11.21522
Data-centric Artificial Intelligence
Jakubik, J.; Vössing, M.; Kühl, N.; Walk, J.; Satzger, G.
Journal Articles
Directed particle swarm optimization with Gaussian-process-based function forecasting
Jakubik, J.; Binding, A.; Feuerriegel, S.
2021. European journal of operational research, 295 (1), 157–169. doi:10.1016/j.ejor.2021.02.053
Journal Articles
Reinforcement learning for opportunistic maintenance optimization
Kuhnle, A.; Jakubik, J.; Lanza, G.
2019. Production Engineering, 13 (1), 33–41. doi:10.1007/s11740-018-0855-7