Prof. Dr. Marco Landt-Hayen
Professor of Business Informatics with a focus on DigitalisationShort Profile
Dr Marco Landt-Hayen has been Professor of Business Informatics at HSBA since December 2025. After completing a degree in physics and a Master of Science in Data Science, he obtained his doctorate in computer science from Christian Albrecht University in Kiel and the Helmholtz School for Marine Data Science (MarDATA) with a thesis entitled: ‘Exploring Methods of Explainable AI - Data-driven Attribution of Climate Events’.
Prof. Dr Marco Landt-Hayen has many years of experience as a data analyst and in the implementation of AI solutions in the fields of automatic speech recognition and image processing, as well as a lecturer at HAW Kiel, including in the online Business Informatics programme.
Publications
Journal articles (peer-reviewed)
2023. "A Climate Index Collection Based on Model Data." Environmental Data Science 2 (e9). https://doi.org/10.1017/eds.2023.5.
2008. "A Rigid Sublimable Naphthalenediimide Cyclophane As Model Compound for UHV STM Experiments." Chemical Communications (Cambridge, England): 2370. https://doi.org/10.1039/b719796a.
Conference proceedings
2023. "A Bottom-up Sampling Strategy for Reconstructing Geospatial Data from Ultra Sparse Inputs." In Lecture Notes in Computer Science. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-46661-8_45.
2023. "CICMoD - a Climate Index Collection Benchmark (data and Resources Paper)." In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM. https://doi.org/10.1145/3589132.3625627.
2023. "Fact or Artifact? Revise Layer-wise Relevance Propagation on Various ANN Architectures⋆." In Computer Science, Engineering and Applications. Academy & Industry Research Collaboration Center. https://doi.org/10.5121/csit.2023.132305.
2023. "Reconstruct Geospatial Data from Ultra Sparse Inputs to Predict Climate Events." In 2023 IEEE 19th International Conference on e-Science (e-Science). IEEE. https://doi.org/10.1109/e-science58273.2023.10254937.
2022. "Layer-wise Relevance Propagation for Echo State Networks Applied to Earth System Variability." In Signal, Image Processing and Embedded Systems Trends. Academy and Industry Research Collaboration Center (AIRCC). https://doi.org/10.5121/csit.2022.122008.
Conference presentations
2023. "Data-driven Attributing of Climate Events with Climate Index Collection Based on Model Data (CICMoD)." Presented at Oral Presentation: EGU General Assembly, Wien. https://doi.org/10.5194/egusphere-egu23-984.
2023. "Reconstruct Missing Data from Sparse Inputs CNNs Find Optimized Sampling Strategy." Presented at Oral Presentation: 54th International Liège Colloquium Ocean Dynamics, Liège.
2022. "Layer-wise Relevance Propagation Echo State Networks Applied Earth System Variability." Presented at Poster Presentation: 7th Data Science Symposium, Hereon, Geesthacht.
2022. "Layer-wise Relevance Propagation Echo State Networks Applied Earth System Variability“, Oral Presentation: AGU Fall Meeting." Presented at Oral Presentation: AGU Fall Meeting, Chicago, IL, USA.