Xipeng Qiu

Associate Professor,Group of Natural Language Processing and Deep Learning
School of Computer Science, Fudan University








Email: xpqiu@fudan.edu.cn

Computer Building, No. 825, Zhangheng Road, Shanghai, China

About Me

Xipeng Qiu is an associate professor at the School of Computer Science, Fudan University. He received his B.S. and Ph.D. degrees from Fudan University in 2001 and 2006, respectively. His research interests include deep learning and natural language processing. He has published more than 40 top journal/conference papers (such as ACL, IJCAI, EMNLP, ICCV, etc.). He is lead developer of the open source project FudanNLP [GitHub] [Google Code] for Chinese language processing.

Keynote/Invited Speaker (recent)

  1. Corpus and Empirical Linguistics Workshop 2016, Jun. 2016, Hongkong. [Slides]
  2. The CCF Advanced Disciplines Lectures on Sentiment Analysis, Jun. 2016, Beijing. [Slides]
  3. The CCF Advanced Disciplines Lectures on Knowledge Graph, Dec. 2015, Beijing. [Slides]
  4. ACML 2015 workshop on Deep Learning, Dec 2015 Hongkong, [Slides]
  5. CCL 2015 (The 13rd China National Conference on Computational Linguistics, Nov 2015, Guangzhou), [Slides]
  6. CCIR 2015 (The 21st China Conference on Information Retrieval, Aug 2015, Luoyang), [Slides]


  1. 2015 Young Talent Program of China Association for Science and Technology

Professional Activity

Committee Memberships

Standing Youth Committee of Chinese Association for Artificial Intelligence (CAAI)

Standing Committee of Natural Language Understanding of CAAI

Youth Committee of the Chinese Information Processing Society of China (CIPSC)

Program Committee

ACL (2014, 2015,2016)

EMNLP (2012, 2013, 2014,2015)

IJCAI (2015,2016)

Area Chair

CCKS (2016), China Conference on Knowledge Graph and Semantic Computing

NLPCC-ICCPOL (2016), The Fifth Conference on Natural Language Processing and Chinese Computing & The Twenty Fourth International Conference on Computer Processing of Oriental Languages


  1. Compilers, for UG students
  2. Neural Networks and Deep Learning, for PG students
  3. Text Management and Analysis, for UG students