Short Bio:
Originally from China, Yunfan Zhang completed his Diplom’s degree in 2023 in Mechanical Engineering at TU Dresden. He worked as a research assistant at Fraunhofer IKTS and HZDR, developing modern deep learning and computer vision algorithms for various industrial scenarios including robotic inkjet printing, battery recycling, and X-ray holography inspection. His Diplom’s thesis introduced a novel generative model architecture for high-quality real space reconstruction of X-ray holography. His main efforts involved pattern recognition, uncertainty analysis, and manufacturing. Since June 2024, he has been a PhD candidate at the Faculty of Civil Engineering and Geosciences, TU Delft, focusing on uncertainty quantification for critical components using probabilistic deep learning approaches. His research aims to bridge conventional probabilistic methods and advanced deep learning and translate these tools to manufacturing to improve quality, reliability, and interpretability; his work is funded by the APRIORI doctoral network and TU Delft.
Research Interests:
Probabilistic modeling, uncertainty quantification, machine learning, deep learning, manufacturing.
Personal links:
TU Delft page