Brief Biography

I obtained my bachelor and master degrees from Nanjing University and PhD degree from Hong Kong University of Science and Technology (HKUST). Prior to joining Southern University of Science and Technology (SUSTech), I have worked in HKUST and Hong Kong Baptist University (HKBU) for about eight years.
My current research interests include artificial intelligence, machine learning, pattern recognition, and data mining. I am especially interested in multi-task learning, transfer learning, deep learning, semi-supervised learning, dimensionality reduction, and metric learning.

News

  • [August 2019] One paper got accepted by EMNLP 2019.
  • [July 2019] One paper got accepted by ACM MM 2019.
  • [April 2019] My paper "Parameter Transfer Unit for Deep Neural Networks" won the best paper award in PAKDD 2019.
  • [November 2018] Two papers got accepted by AAAI 2019.
  • [September 2018] One paper got accepted by NIPS 2018.
  • [July 2018] The survey on multi-task learning has been updated at arXiv.
  • [May 2018] One paper got accepted by ICML 2018.
  • [November 2017] Three papers got accepted by AAAI 2018.
  • [July 2017] A survey on multi-task learning was released at arXiv.
  • [April 2017] Three papers got accepted by IJCAI 2017.
  • Contact

  • Outdated email addresses: zhangyu@cse.ust.hk; yuzhang@comp.hkbu.edu.hk; yuzhangcse@ust.hk; yuzhangcse@cse.ust.hk
  • Publications

    [Google Scholar] [DBLP]

    MTL Survey

  • Yu Zhang and Qiang Yang. A Survey on Multi-Task Learning. arXiv:1707.08114v2, 2018. (This survey will be updated constantly.)
  • Book

  • Qiang Yang, Yu Zhang, Wenyuan Dai, and Sinno Jialin Pan. Transfer Learning. Cambridge University Press, 2020.
  • Refereed Journal Papers

  • 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.
  • Yu Zhang and Qiang Yang. An Overview of Multi-Task Learning. National Science Review (NSR), 5(1): 30–43, 2018. (A Chinese translation can be found here.)
  • 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. (Link)
  • 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. (Link)
  • 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. (Link)
  • 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. (Link)
  • Yu Zhang and Dit-Yan Yeung. Multilabel Relationship Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(2): article 7, 2013. (Link)
  • 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. (Link)
  • Yu Zhang and Dit-Yan Yeung. Semisupervised Generalized Discriminant Analysis. IEEE Transactions on Neural Network (TNN), 22(8): 1207-1217, 2011. (Link)
  • Refereed Conference Papers

  • Yu Zhang and Lei Han. Learning (from) Deep Hierarchical Structure among Features. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), pp. 5837-5844, Honolulu, Hawaii, USA, 2019.
  • Yuan Yao, Yu Zhang, Xutao Li, and Yunming Ye. Heterogeneous Domain Adaptation via Soft Transfer Network. In: Proceedings of the 27th ACM International Conference on Multimedia (MM), Nice, France, 2019.
  • 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 33rd AAAI Conference on Artificial Intelligence (AAAI), pp. 4253-4260, Honolulu, Hawaii, USA, 2019. (A technical report is available at arXiv.) (data)
  • Guangneng 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 (formerly known as WWW), pp. 2822-2829, San Francisco, California, USA, 2019. (A technical report is available at arXiv.)
  • Zheng Li, Xin Li, Ying Wei, Lidong Bing, Yu Zhang, and Qiang Yang. Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), Hong Kong, China, 2019.
  • Yinghua Zhang, Yu Zhang, and Qiang Yang. Parameter Transfer Unit for Deep Neural Networks. In: Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 82-95, Macao, China, 2019. (A technical report is available at arXiv.) (Best Paper Award)
  • 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), pp. 425-430, San Jose, CA, USA, 2019.
  • Yu Zhang, Ying Wei, and Qiang Yang. Learning to Multitask. In: Proceedings of the 32nd Annual Conference on Neural Information Processing Systems (NeurIPS/NIPS), pp. 5776–5787, Montréal, Canada, 2018. (A technical report is available at arXiv.)
  • Ying Wei, Yu Zhang, Junzhou Huang, and Qiang Yang. Transfer Learning via Learning to Transfer. In: Proceedings of the 35th International Conference on Machine Learning (ICML), pp. 5072-5081, Stockholm, Sweden, 2018. (A technical report is available at arXiv.)
  • Kaixiang Mo, Yu Zhang, Shuangyin Li, Jiajun Li, and Qiang Yang. Personalizing a Dialogue System with Transfer Reinforcement Learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 5317-5324, New Orleans, Lousiana, USA, 2018. (A technical report is available at arXiv.)
  • Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, and Qiang Yang. Transferable Contextual Bandit for Cross-Domain Recommendation. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 3619-3626, New Orleans, Lousiana, USA, 2018.
  • Zheng Li, Ying Wei, Yu Zhang, and Qiang Yang. Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 5852-5859, New Orleans, Lousiana, USA, 2018. (code)
  • Guangneng Hu, Yu Zhang, and Qiang Yang. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), Lingotto, Turin, Italy, 2018. (A technical report is available at arXiv.)
  • Yu Zhang and Yuan Jiang. Multimodal Linear Discriminant Analysis via Structural Sparsity. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3448-3454, Melbourne, Australia, 2017.
  • 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 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2237-2243, Melbourne, Australia, 2017.
  • Bo Liu, Ying Wei, Yu Zhang, and Qiang Yang. Deep Neural Networks for High Dimension, Low Sample Size Data. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2287-2293, Melbourne, Australia, 2017.
  • Yu Zhang and Qiang Yang. Learning Sparse Task Relations in Multi-Task Learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), pp. 2914-2920, San Francisco, California, USA, 2017.
  • Ben Tan, Yu Zhang, Sinno Jialin Pan, and Qiang Yang. Distant Domain Transfer Learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), pp. 2604-2610, San Francisco, California, USA, 2017.
  • Shuangyin Li, Yu Zhang, Rong Pan, Mingzhi Mao, and Yang Yang. Recurrent Attentional Topic Model. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), pp. 3223-3229, San Francisco, California, USA, 2017. (Project page)
  • Lei Han, Yu Zhang, and Tong Zhang. Fast Component Pursuit for Large-Scale Inverse Covariance Estimation. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1585-1594, San Francisco, California, USA, 2016. (The first two authors contributed equally) (Link)
  • 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 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 865-874, San Francisco, California, USA, 2016. (Link)
  • Shuangyin Li, Rong Pan, Yu Zhang, and Qiang Yang. Correlated Tag Learning in Topic Model. In: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), New York City, NY, USA, 2016.
  • Lei Han and Yu Zhang. Multi-Stage Multi-Task Learning with Reduced Rank. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), pp. 1638-1644, Phoenix, Arizona, USA, 2016. (Both authors contributed equally)
  • Lei Han and Yu Zhang. Reduction Techniques for Graph-based Convex Clustering. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), pp. 1645-1651, Phoenix, Arizona, USA, 2016. (Both authors contributed equally)
  • Yu Zhang. Parallel Multi-Task Learning. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 629-638, Atlantic City, New Jersey, USA, 2015. (Link)
  • Lei Han and Yu Zhang. Learning Tree Structure in Multi-Task Learning. In: Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 397-406, Sydney, 2015. (Both authors contributed equally) (Link) (Matlab Code)
  • Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu. Differentially Private High-Dimensional Data Publishing via Sampling-Based Inference. In: Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 129-138, Sydney, 2015. (Link)
  • Yu Zhang. Multi-Task Learning and Algorithmic Stability. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 3181-3187, Austin Texas, USA, 2015.
  • Lei Han and Yu Zhang. Learning Multi-Level Task Groups in Multi-Task Learning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 2638-2644, Austin Texas, USA, 2015. (Both authors contributed equally) (Matlab Code)
  • Lei Han and Yu Zhang. Discriminative Feature Grouping. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 2631-2637, Austin Texas, USA, 2015. (Both authors contributed equally) (Matlab Code)
  • Lei Han, Yu Zhang, Guojie Song, and Kunqing Xie. Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), pp. 1854-1860, Quebec City, Quebec, Canada, 2014.
  • Yu Zhang. Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms. In: Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS), pp. 1896-1904, Lake Tahoe, Nevada, USA, 2013.
  • Yu Zhang and Dit-Yan Yeung. Learning High-Order Task Relationships in Multi-Task Learning. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 1917-1923, Beijing, China, 2013.
  • Qing Bao, William K. Cheung, and Yu Zhang. Incorporating Structural Diversity of Neighbors in a Diffusion Model for Social Networks. In: Proceedings of the 2013 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 431-438, Atlanta, USA, 2013. (Best Student Paper Award)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Boosting by Exploiting Task Relationships. In: Proceedings of European Conference on Machine Learning and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 697-710, Bristol, UK, 2012. (Link)
  • Yu Zhang and Dit-Yan Yeung. Overlapping Community Detection via Bounded Nonnegative Matrix Tri-Factorization. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 606-614, Beijing, China, 2012. (Link)
  • Yu Zhang, Dit-Yan Yeung, and Eric P. Xing. Supervised Probabilistic Robust Embedding with Sparse Noise. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), pp. 1226-1232, Toronto, Ontario, Canada, 2012.
  • Yu Zhang and Dit-Yan Yeung. Discriminative Experimental Design. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 585-596, Athens, Greece, 2011. (Link)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Learning in Heterogeneous Feature Spaces. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI), pp. 574-579, San Francisco, California, USA, 2011.
  • Yu Zhang, Dit-Yan Yeung, and Qian Xu. Probabilistic Multi-Task Feature Selection. In: Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS), pp. 2559-2567, Vancouver, Canada, 2010.
  • Yu Zhang and Dit-Yan Yeung. Worst-Case Linear Discriminant Analysis. In: Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS), pp. 2568-2576, Vancouver, Canada, 2010.
  • Yu Zhang and Dit-Yan Yeung. A Convex Formulation for Learning Task Relationships in Multi-Task Learning. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 733-742, Catalina Island, California, 2010. (Best Paper Award) (Matlab Code)
  • Yu Zhang, Bin Cao, and Dit-Yan Yeung. Multi-Domain Collaborative Filtering. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 725-732, Catalina Island, California, 2010.
  • Yu Zhang and Dit-Yan Yeung. Transfer Metric Learning by Learning Task Relationships. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1199-1208, Washington, DC, USA, 2010. (Link)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Warped Gaussian Process for Personalized Age Estimation. In: Proceedings of the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2622-2629, San Francisco, CA, 2010. (Link) (Matlab Code)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Learning using Generalized t Process. In: Proceedings of the 13rd International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 964-971, Chia Laguna Resort, Sardinia, Italy, 2010.
  • Yan-Ming Zhang, Yu Zhang, Dit-Yan Yeung, Cheng-Lin Liu, and Xinwen Hou. Transductive Learning on Adaptive Graphs. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI), pp. 661-666, Atlanta, Georgia, USA. 2010.
  • Bin Cao, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. Adaptive Transfer Learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI), pp. 407-412, Atlanta, Georgia, USA, 2010.
  • 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. (Link)
  • 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, 2009. (Link)
  • 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, 2008. (Link)
  • Yu Zhang and Dit-Yan Yeung. Semi-Supervised Discriminant Analysis using Robust Path-based Similarity. In: Proceedings of the 21st IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, 2008. (Link)
  • 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 14th ACM International Conference on Multimedia (MM), pp. 307-316, Santa Barbara, CA, 2006. (Link)
  • Ph.D. Thesis

  • Yu Zhang. A Probabilistic Framework for Learning Task Relationships in Multi-Task Learning. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, August, 2011. (pdf)
  • Technical Reports

  • Kaixiang Mo, Yu Zhang, Qiang Yang, and Pascale Fung. Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment. arXiv:1804.07691, 2018.
  • Guangneng Hu, Yu Zhang, and Qiang Yang. LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text. arXiv:1804.06201, 2018.
  • Kaixiang Mo, Yu Zhang, Qiang Yang, and Pascale Fung. Fine Grained Knowledge Transfer for Personalized Task-Oriented Dialogue Systems. arXiv:1711.04079, 2017.
  • Weiyan Wang, Yuxiang Wu, Yu Zhang, Zhongqi Lu, Kaixiang Mo, and Qiang Yang. Integrating User and Agent Models: A Deep Task-Oriented Dialogue System. arXiv:1711.03697, 2017.
  • Book Chapters

  • Bin Cao, Yu Zhang, and Qiang Yang. Transfer Learning, Multi-Task Learning and Cost-Sensitive Learning. In Cost-Sensitive Machine Learning, B. Krishnapuram, S. Yu, and B. Rao (ed.), pp. 61-86, CRC Press, 2012. (Link)
  • Dit-Yan Yeung and Yu Zhang. Learning Inverse Dynamics by Gaussian Process Regression under the Multi-Task Learning Framework. In The Path to Autonomous Robots, G. S. Sukhatme(ed.), pp.131-142. Springer, 2009. (Link)
  • Workshop Papers

  • Yang Liu, Zhonglei Gu, Yu Zhang, and Yan Liu. Mining Emotional Features of Movies. In: Proceedings of the MediaEval 2016 Workshop, Hilversum, The Netherlands, 2016.
  • Yu Zhang. Age Estimation using Bayesian Process. In New Frontiers in Applied Data Mining, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Shenzhen, China, 2011. (Link)
  • Papers in Chinese

  • Yu Zhang and Zhi-Hua Zhou. A New Age Estimation Method based on Ensemble Learning. ACTA AUTOMATICA SINICA, 34(8): 997-1000, 2008.
  • Yu Zhang and Zhi-Hua Zhou. Research on Facial Features that Impact Gender Classification. Journal of Jiangsu Polytechnic University, 17: 9–12, 2005. (Best Paper Award by Jiangsu Province Computer Society 2005)
  • Honors

  • Best Paper Award, PAKDD Conference, 2019
  • Young National Distinguished Scholar, China, 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
  • Overseas Research Award, HKUST, 2010-2011
  • Research Travel Award, NIPS, UAI, AISTATS, 2010
  • Champion, Postgraduate Student Research Paper Competition 2010, IEEE (Hong Kong) Computational Intelligence Chapter
  • Research Travel Award, HKUST, 2009
  • Second Runner-Up Award, Postgraduate Student Research Paper Competition 2009, IEEE (Hong Kong) Computational Intelligence Chapter
  • Postgraduate Scholarship, HKUST (2007-2011)
  • Best Paper Award, Jiangsu Province Computer Society, 2005
  • Excellent Undergraduate Scholarship, Nanjing University (2001-2004)
  • Professional Activities

    • Journal Reviewer

      Artificial Intelligence
      IEEE Transactions on Pattern Analysis and Machine Intelligence
      Journal of Machine Learning Research
      Machine Learning
      IEEE Transactions on Knowledge and Data Engineering
      IEEE Transactions on Multimedia
      IEEE Transactions on Neural Networks and Learning Systems
      IEEE Transactions on Systems, Man, and Cybernetics, Part B
      IEEE Transactions on Big Data
      ACM Transactions on Intelligent Systems and Technology
      Neural Computation
      National Science Review
      Statistical Analysis and Data Mining
      Pattern Recognition
      Physica A
      Knowledge and Information Systems
      Pattern Recognition Letter
      Information Sciences
      Neurocomputing
      Applied Intelligence

    • Conference (Senior) PC Member

      IJCAI (2013, 2015, 2016, 2017, 2018, 2019)
      AAAI (2016, 2017, 2018, 2019)
      UAI (2012, 2014, 2015, 2016, 2017, 2018, 2019)
      NIPS/NeurIPS (2013, 2014, 2015, 2016, 2017, 2018, 2019)
      ICML (2014, 2016, 2018)
      AISTATS (2017, 2019)
      ICLR (2018, 2019, 2020)
      KDD (2015, 2016, 2017)
      ICPR (2014, 2016)
      ACML (2013, 2017, 2018, 2019)
      PRICAI (2014, 2016, 2018, 2019)
      NAACL-HLT (2019)
      EMNLP-IJCNLP (2019)
      ICDM (2015)
      SDM (2013)
      CIKM (2017)

    Teaching Experience

  • Advanced Artificial Intelligence, Fall 2019, SUSTech
  • MSBD 5012: Machine Learning, Spring 2018, HKUST
  • COMP 4331: Data Mining, Fall 2017, HKUST
  • MSBD 6000B: Deep Learning, Fall 2017, HKUST
  • COMP 7930: Big Data Analytics, Spring 2015, HKBU
  • COMP 7390: Algorithms for Financial Information Systems, Fall 2014, HKBU
  • COMP 3045 & 3790: Advanced Algorithm Design, Analysis and Implementation, Fall 2014, HKBU
  • COMP 7070: Advanced Topics in Machine Learning, Fall 2013, HKBU
  • COMP 7340: Enterprise Architecture and Integration, Spring 2013, HKBU
  • Short Course: Classic Data Mining Algorithms, Winter 2012, HKBU