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Kai Moltzen, M.Sc.

Portrait
21335 Lüneburg, Universitätsallee 1, C4.308b
Fon +49.4131.677-2417, kai.moltzen@leuphana.de

Vita

Following my Bachelor's in Engineering and Management (B. Eng.) at Esslingen University of Applied Sciences, I completed an M.Sc. in Data Science at Leuphana University Lüneburg. Alongside my studies, I gained industry experience through internships and working student positions at Mercedes-Benz, Ulixes Robotersysteme, and Markt-Pilot.

During both my undergraduate and graduate education, I enjoyed sharing knowledge by conducting tutorials in Mathematics and Machine Learning. I was also actively involved as a student representative and member of various study commissions during my Bachelor's and Master's studies. To broaden my cultural and technical horizons, I spent three semesters abroad at Tampere University (Finland) and Ca' Foscari University of Venice (Italy).

Currently, as a research associate in Leuphana's AIX group, I am pursuing my PhD under the supervision of Ricardo Usbeck. In this role, I continue my passion for teaching across several subjects, contribute to the development of the research group, and conduct research in topics related to GeoAI.

In my research, I am driven by the goal of strengthening societal resilience. Through combining the wealth of geospatial information contained in natural language with our knowledge of the physical world as learned from massive amounts of remote sensing images, I am convinced we can improve our understanding of our environment. This helps a plethora of applications, with my focus lying on the rapid and reliable generation of global disaster information and mitigation strategies. 

Teaching

  • Connecting AI and our Environment: Current Methods in GeoAI (M.Sc.)
  • Explainable Artificial Intelligence (XAI) and Data Visualization (B.Sc.)
  • Advanced Machine Learning - LLMs, RAG, KGs (M.Sc.)
  • AI project (B.Sc.)
  • Foundations of AI (B.Sc.)
  • DataX (B.Sc.)

Research Interests

  • Geospatial Foundation Models (GeoFMs) & Earth Embeddings
  • (Qualitative) Spatial Reasoning
  • Geospatial Knowledge Graphs (GeoKGs)
  • AI to enhance (natural) disaster resilience
  • Spatial Representation Learning
  • Explainable Artificial Intelligence
  • Natural Language Understanding

Projects

  1. Leuphana AI Campus – Minor Artificial Intelligence for Bachelor Students
    Ricardo Usbeck (Project manager, academic) , Anna Ehrenberg (Co-Projectmanager, academic) , Kai Moltzen (Project staff) , Hanna Marlene Schulz (Project staff) , Martin Kohler (Project staff)
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    Project: Teaching

  2. Leuphana Start Week - On the Effects of Human Cooperation with AI Assistants
    Kai Moltzen (Project manager, academic)
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    Project: Teaching

  3. Studienqualitätsmittel: Implementation and Maintenance of a Student AI Server
    Kai Moltzen (Project manager, academic) , Debayan Banerjee (Project manager, academic)
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    Project: Teaching

  4. Studienqualitätsmittel: Student AI server
    Debayan Banerjee (Project manager, academic) , Kai Moltzen (Project manager, academic) , Ricardo Usbeck (Project manager, academic)
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    Project: Teaching

Publications

Contributions to collected editions/anthologies

  1. GeoKG for GeoFM: Neuro-Symbolic Integration of Semantically-Enriched Geospatial-Temporal Data (Short Paper)
    Kai Moltzen (Author) , Debayan Banerjee (Author) , Julian Burmester (Author) , Anna Ehrenberg (Author) , Martin Kohler (Author) , Cedric Möller (Author) , Jann Pfeifer (Author) , Tilahun Taffa (Author) , Hanna Marlene Schulz (Author) , Aida Usmanova (Author) , Patrick Westphal (Author) , Ricardo Usbeck (Author) , 10.09.2026 , p. 20:1-20:9 , 9 p.

    Research output: Contributions to collected editions/anthologies › Article in conference proceedings › Research › peer-review

  2. GANDR - Georelating Dataset, Metrics, and Evaluation
    Kai Moltzen (Author) , Ricardo Usbeck (Author) , 19.12.2025 New York, NY, USA , p. 61-71 , 11 p.

    Research output: Contributions to collected editions/anthologies › Article in conference proceedings › Research › peer-review

  3. LLM Agents for Georelating - A New Task for Locating Events
    Kai Moltzen (Author) , Junbo Huang (Author) , Ricardo Usbeck (Author) , 12.12.2025 New York, NY, USA , p. 277–280 , 4 p.

    Research output: Contributions to collected editions/anthologies › Article in conference proceedings › Research › peer-review

Activities

  1. Posterpresentation LLM-Agents for Georelating
    Kai Moltzen (Speaker)

    Activity: Presentations (poster etc.) › Research

  2. Presentation Leuphana @ Session II Future Knowledge Experts
    Kai Moltzen (Speaker)

    Activity: talk or presentation in privat or public events › Education

Prizes

  1. 2025 Research Award of the School of Management & Technology
    (Recipient) (Recipient) ,

    Prize: Leuphana internal Prize, Scholaships, distinctions, appointments › Research

  2. Award for Completing the Course of Study with Outstanding Success
    (Recipient) ,

    Prize: Leuphana internal Prize, Scholaships, distinctions, appointments › Education

Courses

This seminar deals with the most recent research on geospatial artificial intelligence (GeoAI), which is about developing automatic methods that handle geospatial data to solve real-world problems. Such data range from natural language descriptions of places and events to satellite imagery and weather sensors, to name only a few.
Concretely, we will develop a methodological understanding of AI and Geographical Information Systems (GIS) methods by dissecting foundational research papers and literature in the first part of the seminar. In the second part, students pick a specific societal challenge and focus on the GeoAI approach to solve it, thereby learning in-depth competencies in a field which matters to them. Further, this might act as a stepping stone for students to find a field they want to conduct research on in their thesis and beyond.
Besides this methodological approach, some students can also focus on a more creative examination of GeoAI topics with the objective of developing an interactive installation using existing GeoAI methods or data.
Another key aspect will be the discussion about differences between the considered and related disciplinces (e.g., disaster resilience, mobility, environmental research, flows, remote sensing, etc.) and the backgrounds that the students in this complementary course come from, as well as the specific challenges of ethical considerations towards GeoAI, referred to as GeoAIEthics.
In a total of eight sessions, this course is all about active and informed discussion of the latest research, and connecting the diverse backgrounds and interests of students in this highly interdisciplinary field.
Next appointment:
Wednesday, 2026-10-14 at 10:15