Quy Le Xuan, M. Sc.
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
Germany
phone: +49 511 762-19581
fax: +49 511 762-5333
office location: room 1313
 

Quy Le Xuan studied Electrical Engineering and Information Technology with an emphasis on Communications Engineering at the University of Hannover. He received his B.Sc. and his M.Sc. degrees (both with distinction) from the University of Hannover in November 2017 and October 2020, respectively. His master thesis at Volkswagen Group Innovation dealt with artificial neural network-based multimodal trajectory predictions with HD map cues for highly-automated driving. Since March 2021 he is a member of the group of Professor Ostermann, working as a research assistant toward his PhD. 

Research interests:

  • Deep learning; Representation learning
  • Uncertainty-aware neural networks
  • Transfer learning; Domain adaptation
  • Self-supervised learning
  • Continual/Lifelong learning
  • Interpretable machine learning (iML)
  • AI-driven predictive maintenance (PdM)
    • Prognostics and health management (PHM)
    • Remaining useful life (RUL) prediction
    • Faults detection and diagnostics
    • Anomaly detection

If you are interested in writing a thesis or in a HiWi position, please do not hesitate to contact me.

Show recent publications only
  • Conference Contributions
    • Quy Le Xuan, Yeremia Gunawan Adhisantoso, Marco Munderloh, Jörn Ostermann
      Uncertainty-Aware Remaining Useful Life Prediction for Predictive Maintenance Using Deep Learning
      16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME, 2022
    • Yeremia Gunawan Adhisantoso, Quy Le Xuan, Christoph Kellerman, Marco Munderloh, Jörn Ostermann
      Introduction to Deep Degradation Metric in Smart Production Ecosystems
      16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME, 2022
    • Antonia Breuer, Quy Le Xuan, Jan-Aike Termöhlen, Silviu Homoceanu, Tim Fingscheidt
      Quo Vadis? Meaningful Multiple Trajectory Hypotheses Prediction in Autonomous Driving
      24th IEEE International Conference on Intelligent Transportation Systems, ITSC 2021, Indianapolis, IN, United States, September 2021
  • Journals
    • Quy Le Xuan, Marco Munderloh, Jörn Ostermann
      Self-supervised Domain Adaptation for Machinery Remaining Useful Life Prediction
      Journal on Reliability Engineering and System Safety, Special Issue: RUL Prediction and System Reliability of Complex Systems, Elsevier, Vol. 250, p. 110296, 2024