I am a Researcher of the  Web Learning Group headed by Chris Burges.  Prior to joining Microsoft Research, I worked at the Machine Learning Department of NEC Laboratories America (Princeton campus) with Vladimir Vapnik, and at the Max Planck Institute for Biological Cybernetics, the Empirical Inference Department headed by Bernhard Schölkopf.

My research is centered around  statistic machine learning and its application to web information retrieval. Specifically, I am working on algorithms and theoretic foundations of transductive inference (or semi-supervised learning),  clustering, ranking, kernel machines, active learning, and learning with graphs, and their applications to spam detection, relevance  ranking,  query rewriting, social network analysis, trust and reputation systems, recommendation systems, and online advertising.

NIPS 2007 Workshop: Machine Learning for Web Search

[2008]

• D. Zhou and C. Burges. High-Order Regularization on Graphs.  International Workshop on Mining and Learning with Graphs, Helsinki, Finland, 2008.

[2007]

• Y. Chi, X. Song, D. Zhou, K. Hino, and B. Tseng.  Evolutionary Spectral Clustering by Incorporating Temporal Smoothnes. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007. 

• D. Zhou and C. Burges. Spectral Clustering and Transductive Learning with Multiple Views. Proceedings of the 24th International Conference on Machine Learning, 2007.
• D. Zhou, C. Burges and T. Tao.  Transductive Link Spam Detection.  Proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web, 2007.
• D. Zhou, J. Huang and B. Schölkopf. Learning with Hypergraphs: Clustering, Classification, and Embedding. Advances in Neural Information Processing Systems 19. (Eds.) B. Schölkopf, J.C. Platt and T. Hofmann, MIT Press, Cambridge, MA, 2007.
• G. Camps-Valls, T. V. Bandos and D. Zhou. Semi-Supervised Graph-Based Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing (45), No. 10, 3044-3054, 2007.

 [2006]

• D. Zhou and B. Schölkopf.  Discrete Regularization. Book chapter, Semi-Supervised Learning, 221-232. (Eds.) O. Chapelle,  B. Schölkopf and A. Zien, MIT Press, Cambridge, MA, 2006.
• J. Huang, T. Zhu, R. Rereiner, D. Zhou and D. Schuurmans. Information Marginalization on Subgraphs. Proceedings of 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, 199-210, Springer, New York, NY, USA.
• T. V. Bandos,  D. Zhou and G. Camps-Valls. Semi-Supervised Hyperspectral Image Classification with Graphs. IEEE International Geoscience and Remote Sensing Symposium (IGARSS06), 3883-3886.

 [2005]

• D. Zhou, J. Huang and B. Schölkopf. Learning from Labeled and Unlabeled Data on a Directed Graph. Proceedings of the 22nd International Conference on Machine Learning, 1041-1048. (Eds.) L. De Raedt and S. Wrobel, ACM press, 2005.
• D. Zhou and B. Schölkopf. Regularization on Discrete Spaces. Pattern Recognition, Proceedings of the 27th DAGM Symposium, 361-368, Springer, Berlin, Germany, 2005.
• D. Zhou, B. Schölkopf and T. Hofmann. Semi-Supervised Learning on Directed Graphs. Advances in Neural Information Processing Systems 17, 1633-1640. (Eds.) L.K. Saul,  Y. Weiss and L. Bottou, MIT Press, Cambridge, MA, 2005.
• J. Weston,  C. S. Leslie, E. Ie, D. Zhou, A. Elisseeff and W. S. Noble. Semi-Supervised Protein Classification Using Cluster Kernels. Bioinformatics 21(15), 3241-3247, 2005.
• D. Zhou,  J. Huang and B. Schölkopf.  Beyond Pairwise Classification and Clustering Using Hypergraphs. Max Planck Institute Technical ReportTechnical Report 143, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005.  

[2004]

• D. Zhou , O. Bousquet, T.N. Lal, J. Weston and B. Schölkopf. Learning with Local and Global Consistency. Advances in Neural Information Processing Systems 16, 321-328. (Eds.) S. Thrun,  L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, 2004.
• D. Zhou , J. Weston, A. Gretton, O. Bousquet and B. Schölkopf. Ranking on Data Manifolds. Advances in Neural Information Processing Systems 16, 169-176. (Eds.) S. Thrun,  L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, 2004.
• J. Weston, A. Elisseeff, D. Zhou, C. Leslie and W. S. Noble. Protein Ranking: From Local to Global Structure in the Protein Similarity Network. Proceedings of the National Academy of Science 101(17), 6559-6563, 2004.
• J. Weston , C. Leslie, D. Zhou, A. Elisseeff and W. S. Noble. Semi-Supervised Protein Classification Using Cluster Kernels. Advances in Neural Information Processing Systems 16, 595-602. (Eds.) S. Thrun,  L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, 2004.
• D. Zhou  and B. Schölkopf. A Regularization Framework for Learning from Graph Data. ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields, 2004.
• D. Zhou  and B. Schölkopf. Learning from Labeled and Unlabeled Data Using Random Walks. Pattern Recognition, Proceedings of the 26th DAGM Symposium, 237-244. (Eds.) C.E. Rasmussen, H.H. Bülthoff, M.A. Giese and B. Schölkopf, Springer, Berlin, Germany, 2004.
• K. Yu, V. Tresp and D. Zhou. Semi-Supervised Induction. Max Planck Institute Technical Report 141,  Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2004.

PC member of  ICML 06, ECML 06, AAAI 07,  ICML 07, MLG 07.

Reviewer to NIPS 04, NIPS 05, IJCAI 05, NIPS 06, IJCAI 07, NIPS 07.

Reviewer to Journal of Machine Learning Research, Machine Learning Journal, IEEE Transactions on Information Theory, IEEE Transactions on Pattern Analysis and Machine Intelligence, and IEEE Transactions on Neural Networks.

I like reading, writing, painting, skiing, swimming, hiking, and doing exercises. I am maintaining a personal blog (in Chinese).