Jan Voets, M.Sc
Scientific Researcher
Area of Research:
- Time Series Classification
- Deep Learning and Machine Learning
- Explainable AI
- Computer Vision
Biography
Jan Voets has been a research assistant and doctoral candidate at the Institute for Technologies and Management of Digital Transformation at the University of Wuppertal since June 2025.
Mr. Voets studied industrial engineering with a focus on electrical engineering at the University of Wuppertal in his bachelor's degree and then in his master's degree with a focus on information technology and digitization. In his master's thesis, he dealt with improving the prediction of flood events using deep learning methods by means of generative and augmentative techniques, as well as the training of global prediction models.
He worked as a research assistant at the TMDT during his studies and is now focusing his research on machine learning in an industrial context.
Publications
- 2026
- Voets, J., Tercan, H., Meisen, T., & Esen, C. (2026). "A Systematic Review and Taxonomy of Machine Learning Methods for Process Optimization and Control in Laser Welding" , Applied Sciences , 16 (3), 1568.
- Voets, J., Tercan, H., Meisen, T., & Baum, S. (2026). ConTex: Reformulating Counterfactual Generation For Time Series Forecasting.
- 2025
- Hahn, Y., Voets, J., Königsfeld, A., Tercan, H., & Meisen, T. (2025). "Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding" in Proceedings of the 34th ACM International Conference on Information and Knowledge Management , Cha, Meeyoung and Park, Chanyoung and Park, Noseong and Yang, Carl and Basu Roy, Senjuti and Li, Jessie and Kamps, Jaap and Shin, Kijung and Hooi, Bryan and He, Lifang, Eds. New York, NY, USA : ACM 5699—5706.
ISBN: 9798400720406
