Shaojun Wang 

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Kno.e.sis Center  
Department of Computer Science and Engineering 
Wright State University

Contact Information

• Email: shaojun.wang(at)wright.edu
• Office Phone: (937) 775-5140
• Fax: (937) 775-5133
• WWW Home Page: http://www.cs.wright.edu/~swang
• Mailing Address: Department of Computer Science and Engineering, Wright State University, 3640 Colonel Glenn Hwy., Dayton, Ohio 45435-0001
• Office: 387, Joshi Center

Research Interests

• Statistical machine learning, natural language processing and cloud computing
  

Research Projects

• Semi-supervised discriminative learning and structured prediction
  
• Exploiting syntactic, semantic and lexical regularities in language modeling
  

Teaching

• CS 714 Machine Learning, Winter 2010 (Wright State)
• CS 771 Natural Language Processing Techniques, Winter 2010 (Wright State)
• CS 409/609 Principles of Artificial Intelligence, Fall 2009 (Wright State)
• CS 790 Statistical Learning Theory, Fall 2009 (Wright State)

Education

• Ph.D in Electrical Engineering, University of Illinois at Urbana-Champaign
• M.S in Mathematics, University of Illinois at Urbana-Champaign
• M.S in Electrical Engineering, Tsinghua University, Beijing
• B.S in Electrical Engineering, Tsinghua University, Beijing

Publications

• Selected Publications by Topic
• Publication List

Talks

• Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. [Slides: [ps] [pdf] ], Presented at The 22nd International Conference on Machine Learning , Bonn, Germany, 7-11 August, 2005.

• Learning continuous latent variable models with Bregman divergences. [Slides: [ps]], Presented at The 14th International Conference on Algorithmic Learning Theory, Hokkaido University, Sapporo, Japan, October 17 - 19, 2003.

• The latent maximum entropy principle. [Slides: [ps]], Presented at AMS/IMS/SIAM Joint Summer Research Conference on Machine Learning, Statistics, and Discovery, Snowbird, Utah, June 22-26, 2003.

Quotes

Links

• Johnson Report: Challenges for Theoretical Computer Science
• Challenges in Statistical Machine Learning (video)
• Mathematical Challenges from Genomics and Molecular Biology
  
• Computational Learning Theory
• Game Theory
• The Mathematics of Learning: Dealing with Data
• The language of genes
• Statistical Learning and Data Mining Courses at Waterloo
• Preprocessing text files the easy way
• Twelve definitions of "Classic"(in English) and (in Chinese)
• Knowledge, technique and imagination (in Chinese)
• Mathematics in natural language processing and web search (in Chinese)