Hang Li is chief scientist of the Noah»s Ark Lab of Huawei Technologies. He is also adjunct professor at Peking University, Nanjing University, Xi»an Jiaotong University, and Nankai University. His research areas include information retrieval, natural language processing, statistical machine learning, and data mining. He graduated from Kyoto University in 1988 and earned his Ph D from the University of Tokyo in 1998. He worked at the NEC lab in Japan during 1991 and 2001. He joined Microsoft Research Asia in 2001 and has been working there until present. Hang has about 100 publications at top international journals and conferences, including SIGIR, WWW, WSDM, ACL, EMNLP, ICML, NIPS, and SIGKDD. He and his colleagues» papers received the SIGKDD»08 best application paper award and the SIGIR»08 best student paper award. Hang has also been working on the development of several products. These include Microsoft SQL Server 2005, Microsoft Office 2007 and Office 2010, Microsoft Live Search 2008, Microsoft Bing 2009 and Bing 2010. He has also been very active in the research communities and served or is serving the top conferences and journals. For example, in 2011, he is PC co-chair of WSDM»11; area chairs of SIGIR»11, AAAI»11, NIPS»11; PC members of WWW»11, ACL-HLT»11, SIGKDD»11, ICDM»11, EMNLP»11; and an editorial board member on both the Journal of the American Society for Information Science and the Journal of Computer Science & Technology.
4 Электронные книги Hang Li
Hang Li & Ting Liu: Information Retrieval Technology
This book constitutes the thoroughly refereed post-conference proceedings of the 4th Asia Information Retrieval Symposium, AIRS 2008, held in Harbin, China, in May 2008. The 39 revised full papers an …
PDF
английский
DRM
€115.37
Hang Li: Machine Learning Methods
This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of …
EPUB
английский
DRM
€102.32
Hang Li: Learning to Rank for Information Retrieval and Natural Language Processing
Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing …
PDF
английский
DRM
€29.16