Kai Zhangkzhang2 dot lbl dot gov
PostDoc Researcher, Imaging and Informatics Group, Life Sciences Division Lawrence Berkele National Lab Berkeley, CA, 94720.
BiographyI received the Ph.D. degree in computer science from the Hong Kong University of Science and Technology, Hong Kong, in 2008. Then I joined the Life Science Division, Lawrence Berkeley National Laboratory. My research interests are machine learning and pattern recognition, in particular, large scale unsupervised learning and dimension reduction algorithms. Currently I also work on applications in bioinformatics and complex networks.
- Machine Learning and Data Mining
- Bioinformatics and Computational Biology
- Jie Zhang, Kai Zhang, Jianfeng Feng, Michael SmallUnderstanding Rhythmic Dynamics and Synchronization in Human Gait through Dimensionality Reduction. PLoS Computational Biology, minor revision.
- 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), Bioinformatics [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, Neural Computation [pdf] [matlab codes].
- Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical, IEEE Transactions on Neural Networks.
- Kai Zhang, James T. Kwok. Clustered Nystrom Method for Large Scale Manifold Learning and Dimension Reduction, IEEE Transactions on Neural Networks.
- Kai Zhang, James T. Kwok. Simplifying Mixture Models Through Function Approximation, 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]
- 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: Improved Nystrom low-rank approximation for scalable manifold learning)
- 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]