Nanjing University | China
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Academic Profile of Dr. Yuqi Fang
Early Academic Pursuits
Dr. Yuqi Fang’s academic journey reflects both brilliance and dedication. He earned his B.Sc. in Electrical Engineering from Northeastern University, graduating with an exceptional GPA of 92.4/100, ranking first among 93 peers. His undergraduate thesis focused on EEG-EMG synchronization analysis using Granger causality, laying a strong foundation in biomedical signal processing. He later pursued his Ph.D. in Electronic Engineering at The Chinese University of Hong Kong, where his thesis, “Deep Collaborative Learning for Semantic Segmentation in Medical Images Diagnosis,” showcased his expertise in deep learning, computer-aided medical diagnosis, and medical image analysis.
Professional Endeavors
Following his doctoral studies, Dr. Fang advanced his career through prestigious roles. He served as a Postdoctoral Researcher at The Chinese University of Hong Kong and later at The University of North Carolina at Chapel Hill under renowned supervisors Prof. Kai-yu Tong and Prof. Mingxia Liu. Additionally, he gained valuable industry exposure during his internship at Tencent AI Lab, supervised by Dr. Jianhua Yao. Currently, he holds the position of Tenure-Track Assistant Professor at the School of Intelligence Science and Technology, Nanjing University, where he continues to bridge artificial intelligence with healthcare applications.
Contributions and Research Focus
Dr. Fang’s research lies at the intersection of artificial intelligence, medical image analysis, and human-machine interaction. His major contributions can be categorized into two domains:
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Medical Image Semantic Segmentation – He has pioneered techniques for polyp detection, hepatocellular carcinoma segmentation, pathological image annotation, and diabetic retinopathy lesion analysis. His models, often based on collaborative and multi-view learning, significantly improved clinical diagnosis accuracy and have been published in leading venues such as MICCAI and Medical Image Analysis.
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Multi-Site Data Adaptation for Brain Disease Diagnosis – His groundbreaking studies on fMRI data adaptation, multi-target heterogeneity, and source-free domain adaptation tackle privacy challenges while enhancing diagnostic robustness for diseases like depression and cognitive impairment. His work has appeared in Human Brain Mapping and other high-impact journals.
Additionally, Dr. Fang has engaged in neuroengineering and rehabilitation projects, including transcranial stimulation protocols and exoskeleton-assisted gait training, demonstrating the translational value of his research.
Impact and Influence
Dr. Fang’s research has had a profound academic and clinical impact. His MICCAI paper on polyp segmentation alone has garnered nearly 300 citations, reflecting its global recognition and practical utility. Beyond technical contributions, his emphasis on clinically interpretable AI models has supported biomarker discovery, thereby strengthening the bridge between computational modeling and real-world medical applications.
Academic Citations and Recognition
With over 20 first or co-first author papers in premier journals and conferences, Dr. Fang has established a strong scholarly presence. His works have been cited widely in fields ranging from medical imaging to AI-based diagnostics. Honors such as the Outstanding Tutor Award from CUHK, the First Prize at the International Brain-Computer Interface Hackathon, and multiple scholarships highlight both his academic excellence and mentorship skills.
Teaching and Community Engagement
Beyond research, Dr. Fang has contributed to academia through teaching courses in Biomedical Imaging, Neuroengineering, and Medical Instrument Design. He actively serves as a reviewer for top-tier journals including TPAMI, TMI, MedIA, and TNNLS, further influencing the scholarly community by shaping cutting-edge research.
Legacy and Future Contributions
Dr. Fang’s legacy lies in his innovative integration of AI with clinical medicine, addressing both fundamental challenges and real-world healthcare problems. His ongoing projects in stroke rehabilitation, multimodal gait analysis, and neurostimulation position him to make breakthroughs in personalized healthcare solutions. Looking ahead, his vision includes advancing intelligent medical systems that not only improve diagnosis and prognosis but also actively support clinical decision-making and patient recovery.
Conclusion
Dr. Yuqi Fang’s career exemplifies the power of interdisciplinary innovation, where artificial intelligence converges with medicine to transform healthcare. From his early academic brilliance to his pioneering research contributions, he has consistently pushed the boundaries of medical imaging and brain disease diagnosis. With an impressive record of publications, citations, and recognitions, coupled with a clear vision for future advancements, Dr. Fang stands as a leading figure in the next generation of AI-driven healthcare research.
Notable Publications
“Full Length Article Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline"
- Author: M Yu, Y Fang, Y Liu, AC Bozoki, S Xiao, L Yue, M Liu
- Journal: Neural Networks
- Year: 2025
"Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders"
- Author: Q Wang, W Wang, Y Fang, HJ Li, A Bozoki, M Liu
- Journal: International Symposium on Biomedical Imaging
- Year: 2025
"Attention-enhanced FUSION OF STRUCTURAL AND FUnctional MRI for analyzing HIV-associated asymptomatic neurocognitive impairment"
- Author: Y Fang, W Wang, Q Wang, HJ Li, M Liu
- Journal: International Conference on Medical Image Computing
- Year: 2024
"ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI"
- Author: Y Fang, J Zhang, L Wang, Q Wang, M Liu
- Journal: NeuroImage
- Year: 2024
"Leveraging brain modularity prior for interpretable representation learning of fMRI"
- Author: Q Wang, W Wang, Y Fang, PT Yap, H Zhu, HJ Li, L Qiao, M Liu
- Journal: IEEE Transactions on Biomedical Engineering
- Year: 2024