My major research interests focus on Machine Learning, Data Mining and Information Retrieval.

1.  Some Topics in Multiple Instance Learning, BRNG B260, Purdue University, West Lafayette, 12:00pm-1:00pm, Nov. 7th, 2008 .  (Website, Slides)
2. M3IC: Maximum Margin Multiple Instance Clustering , Pasadena Conference Center, International Joint Conference on Artifical Intelligence (IJCAI)  2009,  Pasadena, CA, 4:00 to 5:30pm, JULY 15, 2009. (Website, Slides)
3. M3IC: Maximum Margin Multiple Instance Clustering , Lawson Computer Science Building 2150,  Purdue University, West Lafayette , 12:30pm - 1:30pm, Nov 9, 2009   (Website, Slides)
4. Serendipitous Learning: Learning Beyond the Predefined Label Space, Purdue Machine Learning Summer School postor session, June 17th, 2011 (Website)
5.Composite Hashing with Multiple Information Sources, SIGIR, grand ballroom of Beijing hotel, Beijing, July 25th, 2011 (Website)
6 .Document Clustering with Universum, , SIGIR, grand ballroom of Beijing hotel, Beijing, July 27th, 2011 (Website)
7 .Transfer Latent Semantic Learning: Microblog Mining with Less Supervision, AAAI, Golden Gate of Hyatt Regency, San Francisco, Aug. 11th, 2011 (Website)
8. Serendipitous Learning: Learning Beyond the Predefined Label Space, SIGKDD, Manchester Grand Hyatt, San Diego, Aug. 22rd, 2011 (Website)
9 .Multi-View Transfer Learning with a Large Margin Approach, SIGKDD, Manchester Grand Hyatt, San Diego, Aug. 23rd, 2011 (Website)
10. Web Recommendation with Insufficient Label Information, SIGKDD, Manchester Grand Hyatt, San Diego, Aug. 23rd, 2011
11. Serendipitous Learning: Learning Beyond the Predefined Label Space, Purdue Machine Learning Seminar , LWSN 3102, Purdue University, Aug 31th, 2011 (Website)
12.Document Clustering with Universum, Center of Information Sciences, HAAS 101, Purdue University, Sep. 2nd, 2011.

Purdue:  CS 580: Algorithm Design, Analysis, and Implementation; STAT 528:  Introduction to Mathematical Statistics; CS 536: Data Communication and Computer Networks; CS 590I: Information Retrieval; CS573/STAT 598M: Data Mining; STAT 598J: Convex Analysis; STAT 598Y: Statistical Learning Theory; CS 502: Compiling and Programming Systems; CS 543 :Introduction to Simulation and Modeling of Computer Systems; CS 514: Numerical Analysis;

Tsinghua: Pattern Recongnition; the Basic of Intelligent Technology; Neural Networks; Modern Signal Analysis; Stochastic Processes, etc.....