Kewei Tu
Assistant Professor, PI
Information Science and Technology
Shanghai Tech University
China
Biography
Kewei Tu received a PhD degree in Computer Science from Iowa State University, USA in 2012. During 2012-2014, he worked as a postdoctoral researcher at the Vision, Cognition, Learning and Art Laboratory, Departments of Statistics and Computer Science of the University of California, Los Angeles, USA. He has been an Assistant Professor, PI with the School of Information Science and Technology at ShanghaiTech University, Shanghai, China since Feb 2014. His main research interests include machine learning of probabilistic grammars with applications in natural language processing and computer vision, and studying grammars as a general model of patterns for different aspects of human cognition.
Research Interest
Grammar Induction, Machine Learning, Natural Language Processing, Computer Vision, Knowledge Representation and Reasoning
Publications
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Kewei Tu and Vasant Honavar, "Unsupervised Learning of Probabilistic Context-Free Grammar Using Iterative Biclustering". In Proceedings of 9th International Colloquium on Grammatical Inference (ICGI 2008), LNCS 5278, St Malo, Brittany, France, September 22-24, 2008.
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Kewei Tu and Vasant Honavar, "On the Utility of Curricula in Unsupervised Learning of Probabilistic Grammars". In Proceedings of the Twenty-second International Joint Conference on Artificial Intelligence (IJCAI 2011), Barcelona, Catalonia, Spain, July 16-22, 2011.
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Kewei Tu and Vasant Honavar, "Unambiguity Regularization for Unsupervised Learning of Probabilistic Grammars". In Proceedings of the 2012 Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL 2012), Jeju, Korea, July 12-14, 2012.
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Kewei Tu, Maria Pavlovskaia and Song-Chun Zhu, "Unsupervised Structure Learning of Stochastic And-Or Grammars". In Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, Nevada, USA, December 5-10, 2013.
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Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe and Song-Chun Zhu, "Joint Video and Text Parsing for Understanding Events and Answering Queires ". To appear in IEEE MultiMedia.
