Shenghuo Zhu

Shenghuo Zhu is a research staff member in NEC Laboratories America, Inc. (Cupertino, CA) since 2004. Before that, he was working on customer behavior research at Amazon.com. He received his Ph.D degree in Computer Science from University of Rochester in 2003.

He is currently working on machine learning, information retrieval, social computing, web intelligence. In addition he is interested in the areas of user modeling, game theory, robotics, machine translation, natural language processing, computer vision, bioinformatics, computer graphics, database, etc.

Selected Publications

the complete list

[*] Shenghuo Zhu, Kai Yu, and Yihong Gong. Stochastic relational models for large-scale dyadic data using MCMC. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, NIPS '08: Advances in Neural Information Processing Systems 21, pages 1993-2000, Cambridge, MA, 2009. MIT Press.
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[*] Shenghuo Zhu, Kai Yu, and Yihong Gong. Predictive matrix-variate t models. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, NIPS '07: Advances in Neural Information Processing Systems 20, pages 1721-1728. MIT Press, Cambridge, MA, 2008.
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[*] Shenghuo Zhu, Tao Li, Zhiyuan Chen, Dingding Wang, and Yihong Gong. Dynamic active probing of helpdesk databases. In VLDB '08: Proceedings of the 34th International Conference on Very Large Data Bases, pages 748-760. VLDB Endowment, 2008.
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[*] Shenghuo Zhu, Kai Yu, Yun Chi, and Yihong Gong. Combining content and link for classification using matrix factorization. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 487-494, New York, NY, USA, 2007. ACM Press.
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[*] Shenghuo Zhu, Xiang Ji, Wei Xu, and Yihong Gong. Multi-labelled classification using maximum entropy method. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 274-281, New York, NY, USA, 2005. ACM.
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Patent and Applications

  1. Identifying early adopters and items adopted by them. United States Patent 7,536,322, granted on May 19, 2009.
  2. Active feature probing using data augmentation. United States Patent Application 20080147852, filed on June 19, 2008.
  3. Systems and methods for generating predictive matrix-variate t models. United States Patent Application 20090099984, filed on April 16, 2009.
  4. Systems and methods for classifying content using matrix factorization. United States Patent Application 20090132901, filed on May 21, 2009.
  5. Monitoring driving safety using semi-supervised sequential learning. United States Patent Application 20090191513, filed on July 30, 2009.
  6. Super Resolution using Gaussian Regression. United States Patent Application 20090274385, filed on November 5, 2009.
  7. Systems and Methods for Processing High-Dimensional Data . United States Patent Application 20090299705, filed on December 3, 2009.
  8. Recommender System with Fast Matrix Factorization using Infinite Dimensions . United States Patent Application 20090299996, filed on December 3, 2009.
  9. Multiple-Document Summarization using Document Clustering. United States Patent Application 20090300486, filed on December 3, 2009.
  10. Finding Communities and Their Evolutions in Dynamic Social Network. United States Patent Application 20100076913, filed on March 25, 2010.
  11. Systems And Methods for Resolution-invariant Image Representation United States Patent Application 20100124383, filed on May 20, 2010.
Last update: Sat Jun 5 16:23:41 PDT 2010

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