Durham University Department of Computer Science
Research Interests
Computer Vision
Machine Learning
Scalable AI
Explainable AI
AI for Healthcare
Zero-Shot Learning
Generative Models
Neuro-Visual Representation Learning
3D Vision
Spatial Intelligence
Health Informatics
Professional Affiliations
- IEEE Senior Member (SMIEEE)
- MRC Innovation Fellow
Academic Honors
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Professional Experience
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Biography
Dr. Yang Long is an Associate Professor in the Department of Computer Science at Durham University. He is an IEEE Senior Member (SMIEEE) and an MRC Innovation Fellow, aiming to design scalable AI solutions for large-scale healthcare applications. His research background lies in the highly interdisciplinary field of Computer Vision and Machine Learning. He is passionate about unveiling the black-box of AI and transferring knowledge to seek Scalable, Interactable, Interpretable, and sustainable solutions for other disciplines, including physical activity, mental health, design, education, security, and geoengineering. He has authored or co-authored over 100 top-tier papers in refereed journals and conferences such as IEEE TPAMI, TIP, CVPR, AAAI, and ACM MM.
Awards & Recognition
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Publications & Representative Works
Complete Bibliography
2026
Sun, Y., Pu, J., Sun, K., Fu, Z., Duan, H., & Long, Y. (2026). Cross-modal progressive modeling for neuro-visual representation learning. Neurocomputing, 683, 133450.
2026
Zhang, T., Wan, F., Miao, X., Deng, J., Xie, X., & Long, Y. (2026). A 2 D 2 C : Adaptive attention-driven dynamic convolution for local feature adaptation. Pattern Recognition, 113915.
Long, Y., Liu, L., Shao, L., Shen, F., Ding, G., & Han, J. (2017). From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis. In Computer Vision and Pattern Recognition (CVPR).
Miao, X., Duan, H., Long, Y., & Han, J. (2025). Rethinking Score Distilling Sampling for 3D Editing and Generation. In Proceedings of the 42nd International Conference on Machine Learning (ICML).
Shao, M., Miao, X., Duan, H., Wang, Z., Chen, J., Huang, Y., Deng, J., Wu, X., Long, Y., & Zheng, Y. (2026). TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2025.
Miao, X., Duan, H., Qian, Q., Wang, J., Long, Y., Shao, L., Zhao, D., Xu, R., & Zhang, G. (2026). Towards Scalable Spatial Intelligence via 2D-to-3D Data Lifting. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV).
Shao, M., Miao, X., Duan, H., Wang, Z., Chen, J., Huang, Y., Deng, J., Wu, X., Long, Y., & Zheng, Y. (2026). TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2025.
