Profile
Dr Pengfei Fan is a Lecturer in Data Science and AI at Queen Mary University of London (QMUL). He earned his PhD from QMUL in 2021, with a thesis focused on learning-based imaging through dynamic scattering media, under the supervision of Prof Lei Su and Prof Kaspar Althoefer. He received both his Bachelor’s and Master’s degrees in Computer Science and Technology from Nanjing University of Science and Technology, China. From 2022 to 2024, Pengfei was an Assistant Professor in the Department of Intelligent Science at Xi'an Jiaotong-Liverpool University, China. Prior to that, he was a Research Associate at the School of Electronic and Optical Engineering, Nanjing University of Science and Technology, China, collaborating with Prof Chao Zuo in the Smart Computational Imaging Lab between 2021-2022. In 2020, he was a Visiting Researcher in the Department of Computer Science and Technology at Tsinghua University, China.
Pengfei is actively involved in the research community as a DERI Fellow, a member of IEEE, IET, BMVA, OSA and SPIE, with affiliations in IAPR, reflecting his expertise in computational imaging, AI, and optics. In education and academic leadership, he is a Fellow of the Higher Education Academy (FHEA), a Committee Member of the QMUL-BUPT Joint Teaching and Learning Centre (JTLC), and an Affiliate Member of the Centre for Excellence in AI in Education at QMUL, contributing to the development of AI-driven education initiatives.
PhD applications through CSC, CONACYT, and HEC are welcome.
Happy to support potential applications for Research Training Fellowships.
Teaching
QHE4102 Introduction to Artificial Intelligence
Research
Research Interests:
Pengfei's research focuses on the development of novel low-level vision and computational imaging technologies, integrating signal processing, machine learning, and computer vision. His work is applied in areas such as medical imaging, image restoration and enhancement, and multimodal imaging fusion. His research has been supported by various domestic and international grants, including those from the EPSRC, Royal Society, NSFC, and Jiangsu Science and Technology Programme.
Research Interests:
Computational Imaging
Low-level Vision
Image Restoration and Enhancement
Inverse Problems
Grants:
Enhancing Data Science Education through Competitive-Based Learning and AI-Driven Assessment, the President and Principal’s Fund for Educational Excellence, 2024 [£20k, PI]
Publications
Full list of publications can be found on Google Scholar.
Fan, P., Wang, Y., Ruddlesden, M., Zuo, C., & Su, L. (2024, May). Enhanced Light Control in Transmission and Reflection through a Dynamically Deformed Multimode Fiber with Deep Learning. In CLEO: Applications and Technology(pp. AF1B-2). Optica Publishing Group.
Fan, P., Wang, Y., Ruddlesden, M., Wang, X., Thaha, M.A., Sun, J., Zuo, C. and Su, L., 2022. Deep learning enabled scalable calibration of a dynamically deformed multimode fiber. Advanced Photonics Research, 3(10), p.2100304.
Zuo, C., Qian, J., Feng, S., Yin, W., Li, Y., Fan, P., ... & Chen, Q. (2022). Deep learning in optical metrology: a review. Light: Science & Applications, 11(1), 1-54.
Fan, P., Ruddlesden, M., Wang, Y., Zhao, L., Lu, C., & Su, L. (2021). Learning enabled continuous transmission of spatially distributed information through multimode fibers. Laser & Photonics Reviews, 15(4), 2000348.
Fan, P., Zhao, T., & Su, L. (2019). Deep learning the high variability and randomness inside multimode fibers. Optics express, 27(15), 20241-20258.
