悉尼科技大学教授，博士生导师，闽江****讲座教授。2015年获得浙江省自然科学一等奖（排名第四），2017年获得谷歌****研究奖(Google Faculty Research Award)和澳大利亚计算机学会数字金质创新奖(ACS Digital Disruptors Awards, Gold Disruptors)。 在国际权威期刊(IEEE Transanctions)和CCFA类会议发表论文250余篇，其中有14篇论文是ESI高引论文。Google Scholar 引用18000余次，H-index为72。杨易教授关于视频跟踪的研究成果被来自美洲、欧洲、亚洲、大洋洲的国家和地区的数十家媒体报道，并被美国霍芬顿邮报评为2013年13大技术创新。2020年杨易教授作为澳大利亚唯一候选人，入选人工智能全球最具影响力****榜单(AI 2000)多领域最具影响力****。近5年来，杨易教授所指导的团队已累计主持科研项目经费超3000余万元。


教育经历：
1999.9-2003.06 浙江大学，计算机科学与技术，本科
2003.09-2010.6 浙江大学，计算机科学与技术，博士


近年来发表的代表性学术论著：
[1] Zheng, Zhedong, Liang Zheng, and Yi Yang. "Unlabeled samples generated by gan improve the person re-identification baseline in vitro." Proceedings of the IEEE International Conference on Computer Vision. 2017.
[2] Sun, Yifan, et al. "Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
[3] Lin, Yutian, et al. "Improving person re-identification by attribute and identity learning." Pattern Recognition 95 (2019): 151-161.
[4] Pan, Pingbo, et al. "Hierarchical recurrent neural encoder for video representation with application to captioning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
[5] Chang, Xiaojun, et al. "Semantic pooling for complex event analysis in untrimmed videos." IEEE transactions on pattern analysis and machine intelligence 39.8 (2016): 1617-1632.
[6] Zhong, Zhun, et al. "Camera style adaptation for person re-identification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
[7] Deng, Weijian, et al. "Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[8] Zheng, Liang, et al. "Pose-invariant embedding for deep person re-identification." IEEE Transactions on Image Processing 28.9 (2019): 4500-4509.
[9] Fan, Hehe, et al. "Unsupervised person re-identification: Clustering and fine-tuning." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14.4 (2018): 1-18.
[10] Zhu, Linchao, et al. "Uncovering the temporal context for video question answering." International Journal of Computer Vision 124.3 (2017): 409-421.