邵杭，男，1993年生，山东威海人，中共党员。青岛大学青年卓越人才，计算机学院人工智能系硕士生导师。2025年博士毕业于南京理工大学计算机科学与技术专业，依托模式识别与智能系统国家重点学科、高维信息智能感知与系统教育部重点实验室、社会安全信息感知与系统工信部重点实验室。主持国家自然科学基金、山东省自然科学基金、青岛市自然科学基金青年项目3项。以第一/通讯作者在IEEE TNNLS、TCSVT、TIM、Pattern Recognition、EAAI、IMAGE等国际权威期刊，以及CVPR、ACM MM等顶级学术会议发表论文10余篇。担任/曾任IEEE TIP、TNNLS、TMM、TCSVT、TFS、TCDS等顶刊审稿人，ACM MM、AAAI等顶会程序委员，阿布扎比MBZUAI机器学习系助理研究员，山东省大创项目指导教师，中电科四十七所《微处理机》青年编委，Journal of Applied Science and Engineering、Frontiers in Mechanical Engineering客座编辑。

研究方向：机器/深度学习、模式识别与计算机视觉、生物特征识别与非接触式多模态感知技术。

招收计算机科学与技术、人工智能、电子信息、软件工程等相关专业硕士研究生，欢迎感兴趣同学联系，具有扎实数学基础者优先。欢迎优秀本科生进组学习，可参与大创、挑战杯等项目或竞赛。

联系方式：shaohang@qdu.edu.cn

教育工作经历：

2025年至今，青岛大学，教师

2021年至2025年，南京理工大学，计算机科学与技术，工学博士

2018年至2021年，上海理工大学，控制科学与工程，工学硕士

2016年至2017年，新疆生产建设兵团司令部直属机关，干部

代表性成果：

[1] H. Shao, et al. PhysFVN: Illumination-aware visible-NIR fusion for remote photoplethysmography via vision-language model. ACM International Conference on Multimedia (CCF-A), Rio de Janeiro, Brazil, 2026.

[2] H. Shao, et al. TranSpike: Pixel-wise frequency reconstruction and spike interaction for remote photoplethysmography. Pattern Recognition (中科院一区Top), 2026.

[3] H. Shao, et al. Remote photoplethysmography in real-world and extreme lighting scenarios. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CCF-A), Nashville, USA, 2025.

[4] H. Shao, et al. Video-based multiphysiological disentanglement and remote robust estimation for respiration. IEEE Transactions on Neural Networks and Learning Systems (中科院一区Top), 2025.

[5] H. Shao, et al. TranPhys: Spatiotemporal masked transformer steered remote photoplethysmography estimation. IEEE Transactions on Circuits and Systems for Video Technology (中科院一区Top), 2024.

[6] H. Shao, et al. TranPulse: Remote photoplethysmography estimation with time-varying supervision to disentangle multiphysiologically interference. IEEE Transactions on Instrumentation and Measurement (中科院二区Top), 2024.

[7] H. Shao, et al. Hyperbolic embedding steered spatiotemporal graph convolutional network for video-based remote heart rate estimation. Engineering Applications of Artificial Intelligence (中科院一区Top), 2023.

[8] C. Zhao, C. Hu, Z. Cao, Y. Song, H. Shao, et al. PipeCSA: Cross-standard adaptation for sewer defect classification via semantic alignment and pseudo labeling. IEEE Transactions on Industrial Informatics (中科院一区Top), 2026.

[9] C. Zhao, C. Hu, H. Shao, et al. PipeMamba: State space model for efficient video-based sewer defect classification. IEEE Transactions on Artificial Intelligence (CAAI-B), 2026.

[10] M. Yan, J. Qian, H. Shao, et al. Self-supervised temperature representation learning for fever screening. IEEE Transactions on Cybernetics (中科院一区Top), 2025.

[11] C. Hu, C. Zhao, H. Shao, et al. TMFF: Trustworthy multi-focus fusion framework for multi-label sewer defect classification in sewer inspection videos. IEEE Transactions on Circuits and Systems for Video Technology (中科院一区Top), 2024.

[12] M. Yan, J. Qian, H. Shao, et al. FeverNet: Enabling accurate and robust remote fever screening. Pattern Recognition (中科院一区Top), 2024.

[13] C. Hu, H. Shao, et al. ASD: Towards attribute spatial decomposition for prior-free facial attribute recognition. IEEE International Conference on Multimedia and Expo (CCF-B), Niagara Falls, Canada, 2024.

[14] C. Hu, B. Dong, H. Shao, et al. Toward purifying defect feature for multilabel sewer defect classification. IEEE Transactions on Instrumentation and Measurement (中科院二区Top), 2023.

[15] C. Zhao, C. Hu, H. Shao, et al. Towards trustworthy multi-label sewer defect classification via evidential deep learning. IEEE International Conference on Acoustics, Speech and Signal Processing (CCF-B), Rhodes Island, Greece, 2023.

[16] C. Hu, K. Chen, H. Shao. A semantic-enhanced method based on deep SVDD for pixel-wise anomaly detection. IEEE International Conference on Multimedia and Expo (CCF-B), Shenzhen, China, 2021.

[17] K. Chen, Y. Wang, C. Hu, H. Shao. Salient object detection with boundary information. IEEE International Conference on Multimedia and Expo (CCF-B), London, UK, 2020.

[18] H. Shao, et al. The 3rd Place of Vision-Based Remote Physiological Signal Sensing Challenge in conjunction with International Joint Conference on Artificial Intelligence (CCF-A) and European Conference on Artificial Intelligence, Bremen, Germany, 2026.

[19] H. Shao, et al. The 2nd Place of Vision-Based Remote Physiological Signal Sensing Challenge in conjunction with International Joint Conference on Artificial Intelligence (CCF-A), Jeju Island, South Korea, 2024.

[20] 一种基于深度迁移学习的汽车钣金件表面缺陷检测方法. 授权发明专利, 2026.

主讲课程：数据科学与Python语言、人工智能概论