Big Data Analytics

Dr. Haiqin Yang
ASCII Haiqin Yang is a postdoctoral fellow at The Chinese University of Hong Kong. His research interests are machine learning, data mining, and big data analytics. He has published two books, seven journal articles, four book chapters, twenty conference papers. He received the first prize award in the IEEE Hong Kong Section 2010 (PG) Student Paper Contest, PCCW Foundation Scholarship, The Global Scholarship Programme for Research Excellence. He served as a chair of IEEE BigData 2013 Workshop, scalable machine learning: theory and applications, PC member of CIKM(2012/2013), ACML(2012/2013), IEEE Big Data2013, IEEE BDDS2013.

Project Introduction

Nowadays, Big Data are generated every day in various domains, such as Internet search, social networks, finance, business sectors, meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research. The huge volume, high velocity, significant variety, and low veracity bring challenges to current machine learning techniques for analyzing the data. The research topics of this project include, but not limited to:

  • Theory and algorithms of data reduce techniques for Big Data
    • Online learning algorithms
    • Data sampling algorithms
  • Large scale network analysis
    • Fusion of information from multiple blogs, rating systems, and social networks
    • Recommendation systems
  • Theory and algorithms of large-scale matrix approximation
    • Online matrix factorization
  • Temporal analysis and longitude study in Big Data
    • Trend prediction in financial data
    • Topic detection in instant message systems
    • Real time modeling of events in dynamic networks
  • Heterogeneous learning on Big multi-modality Data
    • Multitask learning
    • Transfer learning
    • Semi-supervised learning
  • Large scale applications
    • Healthcare
    • Mobile computing such as location-based service, mobile networks, etc.
    • Biological data analysis

Selected Publications

  1. “Sparse Learning Under Regularization Framework: Theory and Applications”, Haiqin Yang, Irwin King and Michael R. Lyu, LAP LAMBERT Academic Publishing, 2011. Book
  2. “Machine Learning: Modeling Data Locally and Globally”, Kai-Zhu Huang, Haiqin Yang, Irwin King and Michael R. Lyu, Springer, 2008.Book
  3. “Efficient Online Learning for Multi-Task Feature Selection”, Haiqin Yang, Michael R. Lyu, and Irwin King, ACM Transactions on Knowledge Discovery from Data, 2013.
  4. “Efficient Sparse Generalized Multiple Kernel Learning,” H. Yang, Z. Xu, J. Ye, I. King, and M.R. Lyu, IEEE Transactions on Neural Network (TNN), vol. 22, no. 3, 2011, pp. 433-446. Paper
  5. “Near-Duplicate Keyframe Retrieval by Semi-Supervised Learning and Nonrigid Image Matching,” J. Zhu, S. Hoi, M.R. Lyu and S. Yan, ACM Transactions on Multimedia Computing, Communications and Applications, vol. 7, no. 1, 2011, pp. 4:1-4:24. Paper
  6. “Response Aware Model-Based Collaborative Filtering,” Guang Ling, Haiqin Yang, M.R. Lyu, and Irwin King, in Proceedings the 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012), Catalina Island, United States, 2012, pp. 501-510. Paper Presentation Poster
  7. “Online Learning for Collaborative Filtering,” Guang Ling, Haiqin Yang, Irwin King and M.R. Lyu, in Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN 2012), Brisbane, Australia, June 10 - 15, 2012, pp. 1-8. Paper
  8. “Can Irrelevant Data Help Semi-supervised Learning, Why and How?,” Haiqin Yang, Irwin King, Shenghuo Zhu and and M.R. Lyu, in Proceeding of the ACM 20th conference on Information and Knowledge Management (CIKM 2011), Glasgow, Scotland, UK, 2011, pp. 937-946. Paper
  9. “Online Learning for Group Lasso,” Haiqin Yang, Zenglin Xu, Irwin King and M.R. Lyu, in Proceedings of the 27th International Conference on Machine Learning (ICML2010), Haifa, Israel, June 2010 Paper
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