Kai Moltzen, M.Sc.

Portrait
21335 Lüneburg, Universitätsallee 1, C4.308b
Fon +49.4131.677-2417, kai.moltzen@leuphana.de
Research associate, Professorship of Information Systems, in particular Artificial Intelligence and Explainability

Table of contents for this page

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.

Teaching

  • Explainable Artificial Intelligence (XAI) and Data Visualization
  • AI project
  • Advanced Machine Learning - LLMs, RAG, KGs
  • DataX

Research Interests

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

Projects

Publications

Contributions to collected editions/anthologies

  1. 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

  2. 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

View all publications

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

View all activities

Prizes

  1. 2025 Research Award of the School of Management & Technology
    Kai Moltzen (Recipient) Ricardo Usbeck (Recipient) ,

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

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

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

View all prizes

Courses

Explainable AI and Visualisation
Kai Moltzen, Ricardo Usbeck
⌄
Aussagekräftige und ansprechende Visualisierungen
- Gestaltungsprinzipien für die Visualisierung quantitativer Daten
- Telling stories with data: What to look for & How to design?
- Im Laufe der Zeit & Proportionen, Unterschiede & (räumliche) Zusammenhänge

Erklärbare KI
- Relevanz und Konzepte der Interpretierbarkeit
- Taxonomie und Evaluation von Erklärungen
- Interpretierbare Modelle, z.B. lineare Regression und Entscheidungsbäume
- Lokale modellagnostische Methoden, z.B. LIME (Local Interpretable Model-Agnostic Explanations),
- Von lokalen zu globalen modellagnostischen Erklärungen mit SHAP (SHapley Additive exPlanations)
- Modellspezifische Erklärungen für Neuronale Netze, z.B. Learned Features und Pixel Attribution Maps
- Erklärbarkeit im Zeitalter von Deep Learning und großen Sprachmodellen (LLMs)
- Aktuelle, relevante Forschungsliteratur
- Regulatorische Anforderungen wie der EU AI Act
- Diskussion über die Zukunft der Mensch-KI-Interaktion, Herausforderungen bei der Erklärbarkeit und weiterführende Ansätze, um die Erklärungen von KI-Modellen transparenter und nachvollziehbarer zu gestalten.
Next appointment:
Thursday, 2026-05-14 at 10:15
Room: C 40.254 Seminarraum
Foundations of AI
Anna Ehrenberg, Martin Jan-Ulrich Kohler, Kai Moltzen, Ricardo Usbeck
⌄
This module introduces key AI concepts, primarily focusing on technical topics such as machine learning, neural networks, and algorithms.

Students will learn methods from regression to advanced techniques in language and image processing, integrating these with an understanding of technological and societal impacts.

The Creative Space for Human and Artificial Intelligence serves as a hub for project-based learning and interdisciplinary collaboration.
Next appointment:
Wednesday, 2026-05-13 at 08:30
Room: C 12.105 Seminarraum
Foundations of AI - Exercise Group 1
Kai Moltzen, Ricardo Usbeck
⌄
The exercise builds on lecture content, offering practical engagement with AI technologies. Students will perform hands-on projects within the Creative Space, involving both software and hardware components, to deploy their project solutions effectively. The exercise accommodates individual learning paths, allowing students to tailor their projects according to their specific interests.
Next appointment:
Wednesday, 2026-05-13 at 10:15
Room: C B.055a Creative Space