Faculty
Research Area:
Artificial Intelligence, Machine Learning, Big Data Analytics
Educational Background
◆ 2007-2011, Ph.D. in Computer Science and Engineering, Hong Kong University of Science and Technology
◆ 2004-2007, M.Eng. in Computer Science, Nanjing University
◆ 2000-2004, B.Sc. in Computer Science, Nanjing University
Professional Experience
◆ 2019-present, Associate Professor, Department of Computer Science and Engineering, Southern University of Science and Technology
◆ 2017-2019, Research Assistant Professor, Department of Computer Science and Engineering, Hong Kong University of Science and Technology
◆ 2015-2017, Research Associate, Department of Computer Science and Engineering, Hong Kong University of Science and Technology
◆ 2012-2015, Research Assistant Professor, Department of Computer Science, Hong Kong Baptist University
◆ 2011-2012, Postdoctoral Research Associate, Department of Computer Science and Engineering, Southern University of Science and Technology
◆ 2011, Visiting Scholar, Machine Learning Department, Carnegie Mellon University
Honors & Awards
◆Top 2% of Global Scientists (Stanford University), 2020 and 2021
◆National Science Review (NSR) 2020 Excellent Paper Award, 2020
◆Choice Outstanding Academic Title 2020, Choice Review, 2020
◆2019 Shenzhen Overseas High-level Talents Class B, 2019
◆National Distinguished Expert (Youth), 2019
◆ Young National Distinguished Scholar, China, 2019
◆ Best Paper Award, PAKDD Conference, 2019
◆ Distinguished Senior Program Committee Member, IJCAI Conference, 2018
◆ Outstanding Reviewer, ICML Conference, 2018
◆ Best Student Paper Award, WI Conference, 2013
◆ Advanced Individual, Shenzhen Virtual University Park, 2013
◆ SENG PhD Research Excellence Award, HKUST, 2011
◆ Best Paper Award, UAI Conference, 2010
◆ Champion, Postgraduate Student Research Paper Competition, IEEE (Hong Kong) Computational Intelligence Chapter, 2010
◆ Second Runner-Up Award, Postgraduate Student Research Paper Competition, IEEE (Hong Kong) Computational Intelligence Chapter, 2009
◆ Best Paper Award, Jiangsu Province Computer Society, China, 2005
Selected Publication Book
[1] Qiang Yang, Yu Zhang, Wenyuan Dai, and Sinno Jialin Pan. Transfer Learning. Cambridge University Press, 2020.
Refereed Journal Papers
[2] Yuan Yao, Xutao Li, Yunming Ye, Feng Liu, Michael K.Ng, Zhichao Huang, and Yu Zhang. Low-Resolution Image Categorization via Heterogeneous Domain Adaptation. Knowledge-Based Systems (KBS), 163(1): 656–665, 2019.(CCF C類)
[3] Yu Zhang and Qiang Yang. An Overview of Multi-Task Learning. National Science Review(NSR),5(1): 30–43, 2018.(JCR 2區(qū))
[4] Qing Bao, William K. Cheung, Yu Zhang, and Jiming Liu. A Component-based Diffusion Model with Structural Diversity for Social Networks. IEEE Transactions on Cybernetics (TCYB),47(4): 1078–1089, 2017.(CCF B類)
[5] Yu Zhang, William K. Cheung, and Jiming Liu. A Unified Framework for Epidemic Prediction based on Poisson Regression. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11): 2878—2892, 2015.(CCF A類)
[6] Deming Zhai, Yu Zhang, Dit-Yan Yeung, Hong Chang, Xilin Chen, and Wen Gao. Instance-Specific Canonical Correlation Analysis. Neurocomputing, 155(1): 205–218, 2015.(CCF C類)
[7] Yu Zhang and Dit-Yan Yeung. A Regularization Approach to Learning Task Relationships in Multitask Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 8(3): article 12, 2014.(CCF B類)
[8] Yu Zhang and Dit-Yan Yeung. Multilabel Relationship Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(2): article 7, 2013.(CCF B類)
[9] Yu Zhang and Dit-Yan Yeung. Transfer Metric Learning with Semi-Supervised Extension. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3): article 54, 2012.(JCR 3區(qū))
[10] Yu Zhang and Dit-Yan Yeung. Semisupervised Generalized Discriminant Analysis. IEEE Transactions on Neural Networks (TNN), 22(8): 1207–1217, 2011.(CCF B類)
[11] 張宇, 周志華. 基于集成的年齡估計(jì)方法. 自動(dòng)化學(xué)報(bào), 34(8): 997–1000, 2008.
Refereed Conference Papers
[12] Yu Zhangand Lei Han. Learning (from) Deep Hierarchical Structure among Features. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI),Honolulu, Hawaii, USA, 29 January–1 February, 2019.(CCF A類)
[13] Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, and Xin Li. Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI),Honolulu, Hawaii, USA, 29 January–1 February, 2019.(CCF A類)
[14] Guang-Neng Hu, Yu Zhang, and Qiang Yang. Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text. In: Proceedings of the Web Conference (WWW), San Francisco, California, USA, 13–17 May, 2019.(CCF A類)
[15] Yinghua Zhang, Yu Zhang, and Qiang Yang. Parameter Transfer Unit for Deep Neural Networks. In: Proceedings of the Twenty-Third Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Macau, China, 14–17 April, 2019.(最佳論文獎(jiǎng))(CCF C類)
[16] Dou Huang, Xuan Song, Zipei Fan, Renhe Jiang, Ryosuke Shibasaki, Yu Zhang, Haizhong Wang, and Yugo Kato. A Variational Autoencoder based Generative Model of Urban Human Mobility. In: Proceedings of IEEE 2nd International Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA, 28–30 March, 2019.
[17] Yu Zhang, Ying Wei, and Qiang Yang. Learning to Multitask. In: Proceedings of the Thirty-Second Annual Conference on Neural Information Processing Systems (NIPS), Montreal, Canada, 3–8 December, 2018.(CCF A類)
[18] Ying Wei, Yu Zhang, Junzhou Huang, and Qiang Yang. Transfer Learning via Learning to Transfer. In: Proceedings of the Thirty-Fifty International Conference on Machine Learning (ICML), pp. 5072–5081, Stockholm, Sweden, 10–15 July, 2018.(CCF A類)
[19] Kaixiang Mo, Yu Zhang, Shuangyin Li, Jiajun Li, and Qiang Yang. Personalizing a Dialogue System with Transfer Reinforcement Learning. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), pp. 5317–5324, New Orleans, Lousiana, USA, 2–7 February, 2018.(CCF A類)
[20] Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, and Qiang Yang. Transferable Contextual Bandit for Cross-Domain Recommendation. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), pp. 3619–3626, New Orleans, Lousiana, USA, 2–7 February, 2018.(CCF A類)
[21] Zheng Li, Ying Wei, Yu Zhang, and Qiang Yang. Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), pp. 5852–5859, New Orleans, Lousiana, USA, 2–7 February, 2018.(CCF A類)
[22] Guang-Neng Hu, Yu Zhang, and Qiang Yang. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation. In: Proceedings of the Twenty-Seventh ACM International Conference on Information and Knowledge Management (CIKM), Lingotto, Turin, Italy, 22–26 October, 2018.(CCF B類)
[23] Yu Zhang and Yuan Jiang. Multimodal Linear Discriminant Analysis via Structural Sparsity. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), pp. 3448–3454, Melbourne, Australia, 19–25 August, 2017.(CCF A類)
[24] Zheng Li, Yu Zhang, Ying Wei, Yuxiang Wu, and Qiang Yang. End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), pp. 2237–2243, Melbourne, Australia, 19–25 August, 2017.(CCF A類)
[25] Bo Liu, Ying Wei, Yu Zhang, and Qiang Yang. Deep Neural Networks for High Dimension, Low Sample Size Data. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), pp. 2287–2293, Melbourne, Australia, 2017.(CCF A類)
[26] Yu Zhang and Qiang Yang. Learning Sparse Task Relations in Multi-Task Learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pp. 2914–2920, San Francisco, California, USA, 4–9 February, 2017.(CCF A類)
[27] Ben Tan, Yu Zhang, Sinno Jialin Pan, and Qiang Yang. Distant Domain Transfer Learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pp. 2604–2610, San Francisco, California, USA, 4–9 February, 2017.(CCF A類)
[28] Shuangyin Li, Yu Zhang, Rong Pan, Mingzhi Mao, and Yang Yang. Recurrent Attentional Topic Model. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pp. 3223–3229, San Francisco, California, USA, 4–9 February, 2017.(CCF A類)
[29] Lei Han, Yu Zhang, and Tong Zhang. Fast Component Pursuit for Large-Scale Inverse Covariance Estimation. In: Proceedings of the Twenty-Second ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1585–1594, San Francisco, California, USA, 13–17 August, 2016. (共同第一作者)(CCF A類)
[30] Lei Han, Yu Zhang, Xiu-Feng Wan, and Tong Zhang. Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data. In: Proceedings of the Twenty-Second ACM SIGKDDConference on Knowledge Discovery and Data Mining (KDD), pp. 865–874, San Francisco, California, USA, 2016.(CCF A類)
[31] Shuangyin Li, Rong Pan, Yu Zhang, and Qiang Yang. Correlated Tag Learning in Topic Model. In: Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), New York City, NY, USA, 25–29 June,2016.(CCF B類)
[32] Lei Han and Yu Zhang. Multi-Stage Multi-Task Learning with Reduced Rank. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp. 1638–1644, Phoenix, Arizona USA, 12–17 February, 2016. (共同第一作者)(CCF A類)
[33] Lei Han and Yu Zhang. Reduction Techniques for Graph-based Convex Clustering. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp. 1645–1651, Phoenix, Arizona USA, 12–17 February, 2016. (共同第一作者)(CCF A類)
[34] Yu Zhang. Parallel Multi-Task Learning. In: Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 629–638, New Jersey, USA, 14–17 Nov 2015.(CCF B類)
[35] Lei Han and Yu Zhang. Learning Tree Structure in Multi-Task Learning. In:Proceedings of the Twenty-First ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 397–406, Sydney, 10–13 August 2015.(共同第一作者)(CCF A類)
[36] Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu. Differentially Private High-Dimensional Data Publishing via Sampling-Based Inference. In:Proceedings of the Twenty-First ACMSIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 129–138, Sydney, 10–13 August 2015.(CCF A類)
[37] Yu Zhang. Multi-Task Learning and Algorithmic Stability. In:Proceedings of theTwenty-Ninth AAAI Conference on Artificial Intelligence(AAAI), pp. 3181–3187, Austin Texas, USA, 25–29 January, 2015.(CCF A類)
[38] Lei Han and Yu Zhang. Learning Multi-Level Task Groups in Multi-Task Learning. In:Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI),pp. 2638–2644, Austin Texas, USA, 25–29 January, 2015. (共同第一作者)(CCF A類)
[39] Lei Han and Yu Zhang. Discriminative Feature Grouping. In:Proceedings of theTwenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), pp. 2631–2637, Austin Texas,USA, 25–29 January, 2015. (共同第一作者)(CCF A類)
[40] Lei Han, Yu Zhang, Guojie Song, and Kunqing Xie. Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework. In:Proceedings of the Twenty-Eighth AAAIConference on Artificial Intelligence (AAAI), pp. 1854–1860, Québec City, Québec, Canada, 27–31July 2014.(CCF A類)
[41] Yu Zhang.Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms. In:Proceedings of the Twenty-Seventh Annual Conference on Neural Information Processing Systems (NIPS), pp. 1896–1904, Nevada, USA, 5–10 December 2013.(CCF A類)
[42] Yu Zhang and Dit-Yan Yeung. Learning High-Order Task Relationships in Multi-Task Learning. In:Proceedings of the Twenty-Third International Joint Conference onArtificial Intelligence (IJCAI), pp. 1917–1923, Beijing, China, 3–9 August 2013.(CCF A類)
[43] Qing Bao, William Cheung, andYu Zhang. Incorporating Structural Diversity ofNeighbors in a Diffusion Model for Social Networks. In:Proceedings of the IEEE/WIC/ACMInternational Conference on Web Intelligence (WI), pp. 431–438,2013.(最佳學(xué)生論文獎(jiǎng))
[44] Yu Zhangand Dit-Yan Yeung. Multi-Task Boosting by Exploiting Task Relationships.In:Proceedings of the European Conference on Machine Learning and Principles andPractice of Knowledge Discovery in Databases (ECML-PKDD), pp. 697–710, Bristol,UK, 24–28 September 2012.(CCF B類)
[45] Yu Zhang and Dit-Yan Yeung. Overlapping Community Detection via Bounded Non-Negative Matrix Tri-Factorization. In:Proceedings of the Eighteenth ACM SIGKDDConference on Knowledge Discovery and Data Mining (KDD), pp. 606–614, Beijing,China, 12–16 August 2012.(CCF A類)
[46] Yu Zhang, Dit-Yan Yeung, and Eric P. Xing. Supervised Probabilistic Robust Embedding with Sparse Noise. In:Proceedings of the Twenty-Sixth AAAI Conference onArtificial Intelligence (AAAI), pp. 1226–1232, Toronto, Ontario, Canada, 22–26 July2012.(CCF A類)
[47] Yu Zhang and Dit-Yan Yeung. Discriminative Experimental Design. In:Proceedings of theEuropean Conference on Machine Learning and Principles and Practice ofKnowledge Discovery in Databases (ECML-PKDD), pp. 585–596, Athens, Greece,2011.(CCF B類)
[48] Yu Zhang and Dit-Yan Yeung. Multi-Task Learning in Heterogeneous Feature Spaces. In:Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence(AAAI), pp. 574–579, San Francisco, California, USA, 7–11 August 2011.(CCF A類)
[49] Yu Zhang, Dit-Yan Yeung, and Qian Xu. Probabilistic Multi-Task Feature Selection. In:Proceedings of the Twenty-Fourth Annual Conference on Neural Information ProcessingSystems (NIPS), pp. 2559–2567, Vancouver, Canada, 6–11 December 2010.(CCF A類)
[50] Yu Zhang and Dit-Yan Yeung. Worst-Case Linear Discriminant Analysis. In:Proceedings of the Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS), pp. 2568–2576, Vancouver, Canada, 6–11 December 2010.(CCF A類)
[51] Yu Zhang and Dit-Yan Yeung. Transfer Metric Learning by Learning Task Relationships. In:Proceedings of the Sixteenth ACM SIGKDD Conference on KnowledgeDiscovery and Data Mining (KDD), pp. 1199–1208, Washington, DC, USA, 25–28July 2010.(CCF A類)
[52] Yan-Ming Zhang, Yu Zhang, Dit-Yan Yeung, Cheng-Lin Liu, and Xinwen Hou. Transductive Learning on Adaptive Graphs. In:Proceedings of the Twenty-Fourth AAAIConference on Artificial Intelligence (AAAI), pp. 661–666, Atlanta, Georgia, USA,July 2010.(CCF A類)
[53] Bin Cao, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. AdaptiveTransfer Learning. In:Proceedings of the Twenty-Fourth AAAI Conference on ArtificialIntelligence (AAAI), pp. 407–412, Atlanta, Georgia, USA, 11–15 July 2010.(CCF A類)
[54] Yu Zhang and Dit-Yan Yeung. A Convex Formulation for Learning Task Relationshipsin Multi-Task Learning. In:Proceedings of the Twenty-Sixth Conference on Uncertaintyin Artificial Intelligence (UAI), pp. 733–742, 2010.(最佳論文獎(jiǎng))(CCF B類)
[55] Yu Zhang, Bin Cao, and Dit-Yan Yeung. Multi-Domain Collaborative Filtering. In: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence(UAI), pp. 725–732, Catalina Island, California, USA, 8–11 July 2010.(CCF B類)
[56] Yu Zhang and Dit-Yan Yeung. Multi-Task Warped Gaussian Process for PersonalizedAge Estimation. In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2622–2629, San Francisco, California, USA, 13–18June 2010.(CCF A類)
[57] Yu Zhang and Dit-Yan Yeung. Multi-Task Learning using Generalized t Process. In:Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 964–971, Sardinia, Italy, 13–15 May 2010.(CCF C類)
[58] Yu Zhang and Dit-Yan Yeung. Semi-Supervised Multi-Task Regression. In:Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 617–631, Bled, Slovenia, 2009.(CCF B類)
[59] Yu Zhang and Dit-Yan Yeung. Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-Supervised Extension. In:Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 602–616, Bled, Slovenia, 7–11 September 2009.(CCF B類)
[60] Yu Zhang and Dit-Yan Yeung. Semi-Supervised Discriminant Analysis via CCCP. In:Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 644–659, Antwerp, Belgium, 15–19 September 2008.(CCF B類)
[61] Yu Zhang and Dit-Yan Yeung. Semi-Supervised Discriminant Analysis using Robust Path-based Similarity. In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, USA, June 2008.(CCF A類)
[62] Xin Geng, Zhi-Hua Zhou, Yu Zhang, Gang Li, and Honghua Dai. Learning from Facial Aging Patterns for Automatic Age Estimation. In:Proceeding of the Fourteenth ACM International Conference on Multimedia (ACMMM), pp. 307–316, Santa Barbara, CA, USA, 23-27 October 2006.(CCF A類)
Others
I am looking for highly motivated Postdoc/PhD/Master/RA. If you are interested, please send me your CV.