Yuqi Fang | Generalization | Best Researcher Award

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Dr. Yuqi Fang | Generalization | Best Researcher Award

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:

  1. 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.

  2. 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

 

 

Zhi Gao | Vision-Language Models | Best Researcher Award

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Dr. Zhi Gao | Vision-Language Models | Best Researcher Award

Postdoctoral Research Fellow at Peking University, China.

Dr. Zhi Gao is a Postdoctoral Research Fellow at the School of Intelligence Science and Technology, Peking University. His research focuses on multimodal learning, vision-language models, and human-robot interaction. With expertise in computer vision and machine learning, he explores the development of intelligent agents capable of understanding and interacting with complex environments.

Professional Profile:

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Education Background 🎓📖

  • Ph.D. in Computer Science and Technology, Beijing Institute of Technology (2018–2023)
  • Master in Computer Science and Technology, Beijing Institute of Technology (2017–2018)
  • B.S. in Computer Science and Technology, Beijing Institute of Technology (2013–2017)

Professional Development 📈💡

Dr. Gao is currently a Postdoctoral Research Fellow at Peking University under the supervision of Prof. Song-Chun Zhu, focusing on multimodal learning and agent development. Concurrently, he serves as a Research Scientist at the Beijing Institute for General Artificial Intelligence, working on vision-language models in the Machine Learning Lab. His research integrates deep learning, data representation, and human-centered AI to enhance machine perception and reasoning.

Research Focus 🔬📖

His work spans computer vision and machine learning, particularly in developing multimodal agents capable of learning from human-robot interactions and adapting to dynamic environments. He is also interested in leveraging the geometry of data space to address challenges such as insufficient annotations and distribution shifts.

Author Metrics

  • Publications in top-tier AI and computer vision conferences and journals
  • Research contributions in multimodal intelligence, vision-language understanding, and AI-driven reasoning

Awards & Honors 🏆🎖️

  • National Science Foundation for Young Scientists of China (2025–2027) for research on Riemannian multimodal large language models for video understanding
  • Distinguished Dissertation Award from SIGAI CHINA (October 202X)

Publication Top Notes

1. A Hyperbolic-to-Hyperbolic Graph Convolutional Network

Authors: Jindou Dai, Yuwei Wu, Zhi Gao, Yunde Jia
Published in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 154-163
Abstract: This paper introduces a hyperbolic-to-hyperbolic graph convolutional network (H2H-GCN) that operates directly on hyperbolic manifolds. The proposed method includes a manifold-preserving graph convolution with hyperbolic feature transformation and neighborhood aggregation, avoiding distortions from tangent space approximations. Extensive experiments demonstrate substantial improvements in tasks such as link prediction, node classification, and graph classification.

openaccess.thecvf.com

2. Curvature Generation in Curved Spaces for Few-Shot Learning

Authors: Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi
Published in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8671-8680
Abstract: This research addresses few-shot learning by proposing task-aware curved embedding spaces using hyperbolic geometry. By generating task-specific embedding spaces with appropriate curvatures, the method enhances the generality of embeddings. The study leverages intra-class and inter-class context information to create discriminative class prototypes, showing benefits over existing embedding methods in both inductive and transductive few-shot learning scenarios.

3. Deep Convolutional Network with Locality and Sparsity Constraints for Texture Classification

Authors: Xiaoyu Bu, Yuwei Wu, Zhi Gao, Yunde Jia
Published in: Pattern Recognition, Volume 91, 2019, Pages 34-46
Abstract: This paper presents a deep convolutional network incorporating locality and sparsity constraints to improve texture classification. The proposed model enhances feature representation by enforcing local connectivity and sparse activation, leading to improved classification performance on texture datasets.

4. Meta-Causal Learning for Single Domain Generalization

Authors: Jianlong Chen, Zhi Gao, Xiaodan Wu, Jiebo Luo
Published in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Abstract: The study introduces a meta-causal learning framework aimed at enhancing generalization in single-domain settings. By leveraging causal relationships within the data, the approach seeks to improve model robustness when applied to unseen domains, addressing challenges in domain generalization.

5. A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold

Authors: Zhi Gao, Yuwei Wu, Mehrtash Harandi, Yunde Jia
Published in: IEEE Transactions on Neural Networks and Learning Systems, Volume 31, Issue 9, 2019, Pages 3230-3244
Abstract: This research proposes a robust distance measure tailored for similarity-based classification tasks on the Symmetric Positive Definite (SPD) manifold. The developed measure enhances classification accuracy by effectively capturing the intrinsic geometry of the SPD manifold, demonstrating robustness in various similarity-based classification scenarios.

Conclusion:

Dr. Zhi Gao is a strong candidate for the Best Researcher Award, given his groundbreaking contributions in vision-language models, hyperbolic learning, and multimodal AI. His strong academic background, top-tier publications, and national recognition make him a well-qualified nominee. However, to further strengthen his impact, he could focus on industry collaborations, real-world AI applications, and global AI leadership.

Verdict: ✅ Highly suitable for the Best Researcher Award with minor areas of improvement for long-term impact.