M.Sc. Victoria Hankemeier
© victoria hangemeier
M.Sc. Victoria Hankemeier
Researcher at the Autonomous Intelligent Systems Group
Raum 202
Einsteinstr. 62
48149 Münster
T: +49-251-8 33 37 03
v.hankemeier@uni-muenster.de

  • Research 

    Research Interests

    • Machine Learning for Spatio-Temporal Prediction: Advancing predictive models for analyzing and forecasting time and space-related data.
    • Uncertainty Quantification in Machine Learning: Developing methods to measure and reduce uncertainty in machine learning predictions.
    • E-Mobility in Logistics: Exploring the electrification of trucks and logistics for sustainable transport solutions, with a focus on their operational environment and impact.
    • EV Charging Infrastructure: Investigating charging station infrastructure to enhance the efficiency of electric vehicle charging processes.

  • CV

    Work Experience

    since 2023 Research Assistant at Autonomous Intelligent Systems Group, Universität Münster
    2022 - 2023 Research Assistant at Institute of Automation and Information Systems, TU Munich

    Education

    2022

    M.Sc., Automation Engineering, RWTH Aachen

     

     

  • Publications

    2024

    B. Lahrsen, B. Vogel-Heuser, J. Wilch, V. Hankemeier, M. Wander and C. Kögel, "Investigating the Adaptability of Alarm Root-Cause Analysis Methods for Discrete Process Types," 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), Bari, Italy, 2024, pp. 1214-1221, doi: 10.1109/CASE59546.2024.10711498.

    B. Vogel-Heuser, A. Fay, B. Rupprecht, F. Kunze, V. Hankemeier, T. Westermann, G. Manca. "Exploring challenges of alarm root-cause analysis across varying production process types" at - Automatisierungstechnik, vol. 72, no. 4, 2024, pp. 369-386. doi: https://doi.org/10.1515/auto-2023-0180

    2022

    M. Trinh, J. Moon, L. Gründel, V. Hankemeier, S. Storms and C. Brecher, "Development of a Framework for Continual Learning in Industrial Robotics," 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, Germany, 2022, pp. 1-8, doi: 10.1109/ETFA52439.2022.9921432.

  • Student Thesis

    If you are a student passionate about delving into the fields of machine learning, time series forecasting, and e-mobility for your thesis, I look forward to hearing from you! Feel free to get in touch, and please include your CV, a short motivational message, and your grade transcript when you do.