Contact Information Zhang Kai, Ph. D. graduate Department of computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong I am currently with the Life Science Division Lawrence Berkeley National Lab. You can reach me via kai_zhang@lbl.gov

Bibliography  [CV]

I am a PhD graduate from the department of computer science and engineering, the Hong Kong University of Science and Technology. I got my master's degree from the National Laboratory of Pattern Recognition, Chinese Academy of Sciences in July, 2004. My advisor is Prof. James T. Kwok.

My PhD thesis Kernel based Clustering and Low Rank Approximation [pdf].

Research Interest [Research Statement]

Machine Learning: Large Scale Clustering, Manifold Learning, Semi-supervised Learning, Kernel Methods, Matrix Decomposition, Nonparametric Density Estimation

Applications: Data Mining, Complex Network, Document Processing, Bioinformatics, Topic Analysis, Speech Recognition

Teaching Assistant

• COMP171 (Fall 2004): Data Structures and Algorithms     
• COMP211 (Spr 2005): Introduction to Software Engineering 
• COMP221 (Spr 2006): Fundamentals of Artificial Intelligence [Tutorial Page]
• COMP328 (Fall 2006): Machine Learning [Tutorial Page]
• COMP171 (Spr 2007): Data Structures and Algorithms
• COMP344 (Fall 2007): Digital Image Processing [Tutorial Page] 
• COMP231 (Spr 2008): Database Management Systems

Research Papers

• Journal papers

  Kai Zhang, Joe W. Gray and Bahram Parvin. Sparse Multitask Regression for Identifying Common Mechanism of Response to Theraputic Targets, the 18th International Conference on Intelligent Systems for Molecular Biology (ISMB2010), Boston, July, 2010 [pdf].

   J. Zhang, K. Zhang, X. Xu, C.K. Tse and M. Small, Seeding the kernels in graphs: towards multi-resolution community analysis. New Journal of Physics, 2009 [pdf].
  
  J. Zhang, J. Sun, X. Luo, K. Zhang, T. Nakamura and M. Small, Characterizing topology of pseudoperiodic time series via complex network approach. In press Physica D [pdf] (2008).

  Kai Zhang, James T. Kwok. Density-Weighted Nystrom Method for Computing Large Kernel Eigen-Systems, accepted by Neural Computation [pdf] [matlab codes].

  Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical, accepted by IEEE Transactions on Neural Networks.

  Kai Zhang, James T. Kwok, Bahram Parvin. Clustered Nystrom Method for Scalable Manifold Learning and Dimension Reduction, conditionally accepted by IEEE Transactions on Neural Networks.

  Kai Zhang, James T. Kwok. Simplifying Mixture Models Through Function Approximation, accepted by IEEE Transactions on Neural Networks. [pdf] [code1] (GMM with identical, spherical covariances) [code2] (GMM with varying, full covariances).

  J. Zhang, M. Small and K. Zhang. "Chaos inducement and enhancement through weak periodic/quasiperiodic perturbations in discrete nonlinear systems." International Journal of Bifurcations and Chaos, 16 5 (2006): 1585-1598. [pdf]

• Conference papers

Kai Zhang, James T. Kwok, Bahram Parvin. Prototype Vector Machine for Large Scale Semi-supervised Learning. In the 26th International Conference on Machine Learning (ICML 2009), Montreal, Canada, June 2009. [pdf] [slides]

Kai Zhang, Ivor W. Tsang, James T. Kwok. Improved Nystrom Low Rank Approximation and Error Analysis. In the 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland, June 2008 [pdf] [slides]

Project page: applying Improved Nystrom low-rank approximation for scalable manifold learning.  (codes updated, some bugs removed now)  

Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical. In the 24th International Conference on Machine Learning (ICML 2007), Oregen, USA, June 2007. [pdf] [poster] [matlab codes (updated)]

Kai Zhang, James T. Kwok. Simplifying Mixture Models Through Function Approximation. In the Neural Information Processing Systems (NIPS2006), Vancouver, Canada, December 2006. [pdf] [poster]

Kai Zhang, James T. Kwok. Block-Quantized  Kernel Matrix for Fast Spectral Embedding. In the 23rd International Conference on Machine Learning (ICML 2006), Pittsburgh, PA, USA, June 2006. [pdf] [slides]

Kai Zhang, James T. Kwok, M. Tang. Accelerated Convergence Using Dynamic Mean Shift. In the 9th European Conference on Computer Vision (ECCV 2006), Graz, Austria, May 2006. [pdf] [poster] [codes]

Ivor W. Tsang, James T. Kwok, Brian Mak, Kai Zhang, Jeffrey J. Pan. Fast Speaker Adaptation via Maximum Penalized Likelihood Kernel Regression. In the International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06), Toulouse, France, May 2006. [pdf]

Kai Zhang, M. Tang, J.T. Kwok. Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation. In the International Conference on Computer Vision and Pattern Recognition (CVPR 2005), San  Diego, CA, USA, June 2005. [pdf]

Intern Experience

June ~ September, 2007, Google Inc., Mountain View, CA (Supervisor: Phil Long).

                                                               Last modified by Kai Zhang at 2/11/2008. All Rights Reserved.