北京邮电大学教授，工学博士，博士生导师。主要研究领域：计算机视觉、机器学习、医疗AI。主持/参与国家自然科学基金面上、国家重点研发计划、北京市自然科学基金、教育部博士点基金等国家级/省部级/企业项目。主要研究工作发表在IEEE TPAMI、IEEE TIP、IEEE TIFS、IEEE TAC、CVPR、MICCAI等国际权威期刊和顶级会议，多篇入选ESI高被引论文。指导研究生获得CVPR 2025（光场图像超分辨率）、CVPR 2023（光场图像超分辨率）、ECCV 2022（3D点云实例分割）、ICCV 2021（3D点云语义分割）等多个AI顶会国际竞赛冠军。CCF计算机视觉专委会委员、CCF多媒体专委会委员、CAA模式识别与机器智能专委会委员、CSIG视觉大数据专委会委员。目前担任IEEE Transactions on Affective Computing、Neurocomputing等国际期刊编委。

部分论著如下：

[1]  Yonghong Li, Shan Qu, and Xiuzhuang Zhou. “Conformal Depression Prediction,” IEEE Transactions on Affective Computing, 2025.

[2]  Yonghong Li, Zeqiang Wei, Guodong Guo, and Xiuzhuang Zhou. “MemRank: Memory-augmented similarity ranking for video-based depression severity estimation,” IEEE Transactions on Affective Computing, 2025.

[3]  Zheng Zhang, Guanchun Yin, Bo Zhang, Wu Liu, Xiuzhuang Zhou, Wendong Wang. “A semantic knowledge complementarity based decoupling framework for semi-supervised class-imbalanced medical image segmentation,” CVPR, 2025.

[4]  Zeyi Hou, Ning Lang, and Xiuzhuang Zhou. “WL-GAN: Learning to sample in generative latent space,” Information Sciences, 2025.

[5]  Ximiao Zhang, Min Xu, and Xiuzhuang Zhou. “RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection,” CVPR, 2024.

[6]  Yu Bai, Bo Zhang, Zheng Zhang, Shuo Yan, Zibo Ma, Wu Liu, Xiuzhuang Zhou, Xiangyang Gong, and Wendong Wang. “Norma: A Noise Robust Memory-Augmented Framework for Whole Slide Image Classification,” ECCV, 2024.

[7]  Min Xu, Ximiao Zhang, and Xiuzhuang Zhou. “Confidence-Calibrated Face and Kinship Verification,” IEEE Transactions on Information Forensics & Security, 2024.

[8]  Mingyue Niu, Ya Li, Jianhua Tao, Xiuzhuang Zhou, Björn W Schuller. “DepressionMLP: A Multi-Layer Perceptron Architecture for Automatic Depression Level Prediction via Facial Keypoints and Action Units,” IEEE Transactions on Circuits and Systems for Video Technology, 2024.

[9]  Zeyi Hou, Ruixin Yan, Ziye Yan, Ning Lang, and Xiuzhuang Zhou. "Energy-Based Controllable Radiology Report Generation with Medical Knowledge," International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024.

[10] Ximiao Zhang, Min Xu, Dehui Qiu, Ruixin Yan, Ning Lang, and Xiuzhuang Zhou. "Mediclip: Adapting clip for few-shot medical image anomaly detection," International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024.

[11] Zeqiang Wei, Kai Jin, Zheng Zhang, and Xiuzhuang Zhou. “Multi-label contrastive hashing,” Pattern Recognition, 2023.

[12] Zeyi Hou, Ruixin Yan, Qizheng Wang, Ning Lang, and Xiuzhuang Zhou. “Diversity-Preserving Chest Radiographs Generation from Reports in One Stage,” International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023.

[13] Xiuzhuang Zhou, Zeqiang Wei, Min Xu, Shan Qu, and Guodong Guo. “Facial Depression Recognition by Deep Joint Label Distribution and Metric Learning,” IEEE Transactions on Affective Computing, 2022.

[14] Bo Zhang, Yunpeng Tan, Hui Wang, Zheng Zhang, Xiuzhuang Zhou, Jingyun Wu, Yue Mi, Haiwen Huang, and Wendong Wang. “LSRML: A Latent Space Regularization based Meta-Learning Framework for MR Image Segmentation,” Pattern Recognition, 2022.

[15] Peng Huang, Xiuzhuang Zhou, Zeqiang Wei, and Guodong Guo. “Energy-based supervised hashing for multimorbidity image retrieval,” International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021.

[16] Xiuzhuang Zhou, Kai Jin, Yuanyuan Shang, and Guodong Guo. "Visually interpretable representation learning for depression recognition from facial images," IEEE Transactions on Affective Computing, 2020.

[17] Xiuzhuang Zhou, Kai Jin, Qian Chen, Min Xu, and Yuanyuan Shang. "Multiple face tracking and recognition with identity-specific localized metric learning," Pattern Recognition, 2018.

[18] Haibin Yan, Jiwen Lu, and Xiuzhuang Zhou. "Prototype-based discriminative feature learning for kinship verification," IEEE Transactions on Cybernetics, 2015.

[19] Jiwen Lu, Venice Erin Liong, Xiuzhuang Zhou, and Jie Zhou. "Learning compact binary face descriptor for face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015.

[20] Jiwen Lu, Xiuzhuang Zhou, Yap-Pen Tan, Yuanyuan Shang, and Jie Zhou. "Neighborhood repulsed metric learning for kinship verification," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.

[21] Haibin Yan, Jiwen Lu, Weihong Deng, and Xiuzhuang Zhou. "Discriminative multi-metric learning for kinship verification," IEEE Transactions on Information Forensics and Security, 2014.

[22] Weihong Deng, Jiani Hu, Xiuzhuang Zhou, and Jun Guo. "Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning," Pattern Recognition, 2014.

[23] Jiwen Lu, Xiuzhuang Zhou, Yuanyuan Shang, Yap-Pen Tan, and Jie Zhou. "Cost-sensitive semi-supervised discriminant analysis for face recognition." IEEE Transactions on Information Forensics & Security, 2012.

[24] Xiuzhuang Zhou, Yao Lu, Jiwen Lu, and Jie Zhou. "Abrupt motion tracking via intensively adaptive Markov chain Monte Carlo sampling," IEEE Transactions on Image Processing, 2012.

[25] Xiuzhuang Zhou, Jiwen Lu, Junlin Hu, and Yuanyuan Shang. "Gabor-based gradient orientation pyramid for kinship verification under uncontrolled environments," ACM International Conference on Multimedia, 2012.

[26] Xiuzhuang Zhou, Junlin Hu, Jiwen Lu, Yuanyuan Shang, and Yong Guan. "Kinship verification from facial images under uncontrolled conditions," ACM International Conference on Multimedia, 2011.

[27] Xiuzhuang Zhou and Yao Lu. "Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling," CVPR, 2010.

[28] 周修庄, 鲁继文. 视觉跟踪中的马氏链蒙特卡洛方法[M]. 北京邮电大学出版社. 2018.