Prof. Dr. Marco Landt-Hayen
Professor für WirtschaftsinformatikKurzprofil
Dr. Marco Landt-Hayen ist seit Dezember 2025 Professor für Wirtschaftsinformatik an der HSBA. Nach einem Diplom-Studium der Physik und einem Master of Science in Data Science promovierte er an der Christian-Albrechts-Universität zu Kiel und der Helmholtz School for Marine Data Science (MarDATA) im Bereich Informatik zum Thema: „Exploring Methods of Explainable AI - Data-driven Attribution of Climate Events“.
Prof. Dr. Marco Landt-Hayen hat langjährige Erfahrung als Data Analyst und mit der Implementierung von KI-Lösungen in den Bereichen automatische Spracherkennung und Bildverarbeitung, sowie als Lehrbeauftragter an der HAW Kiel u.a. im Online-Studiengang Wirtschaftsinformatik.
Lehrveranstaltungen
- Artificial Intelligence and Data Science
- Programmierung II
- Software Engineering
Forschungsschwerpunkte
- Data Science
- Künstliche neuronale Netzwerke
- Deep Learning / Machine Learning
- Explainable AI
Publikationen
Zeitschriftenartikel (referiert)
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.
Konferenzbeiträge
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.
Präsentationen auf Konferenzen
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.