Skip to main content

User account menu

  • Log in
DBS-Logo

Database Group Leipzig

within the department of computer science

ScaDS-Logo Logo of the University of Leipzig

Main navigation

  • Home
  • Study
    • Exams
      • Hinweise zu Klausuren
    • Courses
      • Current
    • Modules
    • LOTS-Training
    • Abschlussarbeiten
    • Masterstudiengang Data Science
    • Oberseminare
    • Problemseminare
    • Top-Studierende
  • Research
    • Projects
      • Benchmark datasets for entity resolution
      • FAMER
      • HyGraph
      • Privacy-Preserving Record Linkage
      • GRADOOP
    • Publications
    • Prototypes
    • Annual reports
    • Cooperations
    • Graduations
    • Colloquia
    • Conferences
  • Team
    • Erhard Rahm
    • Member
    • Former employees
    • Associated members
    • Gallery

Häntschel, Tim

Breadcrumb

  • Home
  • Häntschel, Tim

Publications (4)

Dateien Cover Beschreibung Jahr
PiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing
Uhrich, B. ; Häntschel, T. ; Rahm, E.
arXiv
2026 / 3
Neural Diffusion Graph Convolutional Network for Predicting Heat Transfer in Selective Laser Melting
Uhrich, B. ; Häntschel, T. ; Schäfer, M. ; Rahm, E.
18th International Symposium on Artificial Intelligence and Mathematics (ISAIM 2024)
2024 / 7
Using Differential Equation Inspired Machine Learning for Valve Faults Prediction
Uhrich, B. ; Hlubek, N. ; Häntschel, T. ; Rahm, E.
2023 IEEE 21st International Conference on Industrial Informatics (INDIN)
2023 / 8
Privacy-Preserving Record Linkage using Autoencoders
Christen, V. ; Häntschel, T. ; Christen, P. ; Rahm, E.
International Journal of Data Science and Analytics
2022 / 12

Recent publications

  • 2026 / 3: The use of differential privacy for privacy-preserving record linkage: Protecting the bits but not the people
  • 2026 / 3: PiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing
  • 2026 / 3: Efficient Model Repository for Entity Resolution: Construction, Search, and Integration
  • 2026 / 3: Can Knowledge of Demographics and Privacy Parameters Break Location Privacy?
  • 2026 / 2: A Metamodeling Framework for Accelerated Energy Market Optimization using Active Learning

Footer menu

  • Directions
  • Contact
  • Impressum