AUTHOR PROFILE
ACADEMIC AND PROFESSIONAL BACKGROUND
Fangyu Wu is a distinguished researcher and academic in the field of computer science, specializing in deep learning, multi-modal learning, and intelligent data analysis. He is currently an Associate Professor at Xi’an Jiaotong-Liverpool University (XJTLU) in China, where he supervises PhD and Master's students focusing on innovative research topics such as multi-modal learning and deep learning for computer vision. His previous role included co-supervising PhD students at Zhejiang University, contributing to advancements in facial recognition and image-text retrieval.
HONORS AND AWARDS
Dr. Wu's achievements have been recognized through several prestigious awards. He was named a Suzhou Youth Innovation Leading Talent in 2023 and won first prize at the 7th China Innovation Challenge for his project on intelligent tracking systems using infrared thermal imaging. Additionally, he received the Lotfi Zadeh Best Paper Award at ICMLC&ICWAPR 2017 and has been honored with the Outstanding Graduates award from Xi’an Jiaotong-Liverpool University and National Encouragement Scholarships from China.
RESEARCH PROJECTS
Fangyu Wu leads several high-impact research projects. These include “Intelligent Multimodal Data Analysis for Digital Twin Cities” under the Gusu Innovation and Entrepreneurship Leading Talents Programme, and “Relational Modeling and Reasoning for Reliable Cross-Modal Retrieval” funded by the Zhejiang Natural Science Foundation. His projects also cover advanced topics such as distributed AI platforms for Metaverse scenarios and optimization software for injection molding processes.
PUBLICATIONS
Dr. Wu has an extensive list of publications in top-tier conferences and journals. Notable works include papers on fine-grained image-text matching, relation-aware prototype networks, and pose-robust face recognition. His research has been featured at prestigious conferences such as CVPR, ECCV, and ICPR, showcasing his contributions to advancements in deep learning and computer vision.
CONFERENCE ORGANIZATION
In addition to his research, Fangyu Wu plays a vital role in organizing academic conferences. He served as the Publication Chair for the IEEE 17th International Conference on Computer Science & Education (ICCSE 2022) and as General Co-Chair for the 5th International Symposium on Emerging Technologies for Education (SETE 2020). His involvement ensures the smooth execution of these events and contributes to the dissemination of cutting-edge research.
STUDENT SUPERVISION
Fangyu Wu is actively engaged in supervising students at both the PhD and Master’s levels. He currently supervises a PhD student at XJTLU focusing on multi-modal learning and has previously co-supervised a PhD student at Zhejiang University on deep learning for computer vision. His mentorship extends to six Master’s students at XJTLU and three at Zhejiang University, covering areas such as facial recognition and image-text retrieval.
COMPETITIONS AND RECOGNITION
Dr. Wu has achieved notable success in various competitions. His project on human motion recognition based on deep neural networks won third prize at the China First Smart Manufacturing and Big Data Innovation Competition. Additionally, his participation in competitions has been marked by significant awards, including the first prize in the China Innovation Challenge for his intelligent tracking system.
NOTABLE PUBLICATION
- Fine-grained Image-text Matching by Cross-modal Hard Aligning Network
- Authors: Pan, Z., Wu, F., Zhang, B.
- Year: 2023
- Conference: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
- Pages: 19275–19284
- Knowledge-embedded Prompt Learning for Zero-shot Social Media Text Classification
- Authors: Li, J., Chen, Q., Wang, W., Wu, F.
- Year: 2023
- Conference: IEEE International Conference on Smart Computing (SMARTCOMP)
- Pages: 222–224
- Kernel Triplet Loss for Image-Text Retrieval
- Authors: Pan, Z., Wu, F., Zhang, B.
- Year: 2022
- Conference: Computer Animation and Virtual Worlds
- Article: e2093
- FaceCaps for Facial Expression Recognition
- Authors: Wu, F., Pang, C., Zhang, B.
- Year: 2021
- Conference: Computer Animation and Virtual Worlds
- Article: e2021