Short Bio:
Originally from China, Yunfan obtained his Bachelor’s degree in Process Equipment and Control Engineering in 2019 from the China University of Petroleum (East China). He then finished his Diplom’s degree (equivalent to a Master’s degree) in 2023 in Mechanical Engineering with a specialization in Manufacturing Technology at Dresden University of Technology (TU Dresden). During this period, Yunfan worked as a research assistant at Fraunhofer Institute for Machine Tools and Forming Technology (IWU) and Helmholtz-Zentrum Dresden-Rossendorf (HZDR). He finished his Diplom’s thesis under the co-supervision of TU Dresden and HZDR, having the topic “High-Quality Reconstruction of Real Space Structures from X-ray Holography Using Conditional Wavelet Flow.” In this work, he developed a generative deep learning model for reverse image reconstruction.
In June 2024, Yunfan began his PhD journey at the Faculty of Civil Engineering and Geosciences at Delft University of Technology (TU Delft). His research is focused on “Uncertainty Quantification and Reduction for Industrial Critical Components Using Data-Driven Approaches.” This has the aim of enhancing the quality and reliability of manufacturing sectors by introducing the idea of uncertainty quantification and developing context-based machine learning algorithms. His work is funded by the APRIORI doctoral network for the first three years and by TU Delft for the final year.
Research Interests:
- Probabilistic modeling
- Uncertainty quantification
- Machine learning
- Deep learning
- Manufacturing.
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