Jianchao (Jack) Han

Title: Assistant Professor
Department: Computer Science
Phone: 310-243-2624
Email: jhan@csudh.edu
Office Location: NSM A-133
Laboratory Location: LIB

Research Interests:

Artificial Intelligence, Machine Learning, Expert Systems, Knowledge Discovery

Data Mining, Web/Text/Document Mining and Applications ini Network Security and Bioinformatics

Data and Knowledge Visualization, Human-machine Interface

Rough Set and Fuzzy Set Theories, and Soft Cmoputing

DBMS, SQL, Data Warehousing, OLAP, Multidimensional Data Analysis

Highest degree obtained: Ph.D.

Discipline: Computer Science

Institution/Year: University of Waterloo, Canada / 2001

Thesis Title/Director: Interactive Data Mining and Visualization

Master's Degree:

Discipline: Computer Science

Institution/Year: Chinese Academy of Sciences

Thesis Title/director: Inductive Learning from Examples

Bachelor's Degree:

Discipline: Computer Science and Technology

Institution/Year: Harbin Institute of Technology, China

Positions since highest degree earned:

Assistant Professor of Computer Science, CSUDH

Grants:

•CSUPERB Faculty-Student Collaborative Research Seed Grant (2006-2007): $10,000
•CSUDH- Sally Casanova Memorial RSCAAP Summer Fellowship (2005): $2250
•California State University Pre-Doctor Program Mentor’s Travel Grant (2005): $1000
•CSU Program for Education and Research in Biotechnology (CSUPERB) Fall 2004 Travel Grant (2004): $1000
•CSUDH- Sally Casanova Memorial RSCAAP Summer Fellowship (2004): $4500
•CAS-CSUDH Fund for Faculty Excellence (2003): $1200
•Ontario Graduate Scholarship, Canada (2000-2001): $12000
•Graduate Scholarship of the University of Waterloo, Canada (1998 – 1999) $24000

Awards & Recognitions: 

Best Overall Paper Award: Extracting and Mining Protein-Protein Interaction Network from Biomedical Literature, with Hu, X., Yoo, I., Song, I., Song, M., and Lechner, M., IEEE International Symposium on Computational Intelligence in Bioinformatics and Computational Biology, October 7-8, 2004, La Jolla, California.

Best Paper Award: CViz: An Interactive Visualization System for Rule Induction, with An, A. and Cercone, N., Canadian Conf. on Artificial Intelligence, May 14-17, 2000, Montreal, Canada.

Student Travel Award: RuleViz: A Model for Visualizing Knowledge Discovery Process, International. Conference on Knowledge Discovery and Data Mining, August, 20-24, 2000, Boston, USA.

Publications:

1. Han, J., Beheshti, M., Kowalski, K., Detecting Network Intrusions Based on a Generalized Rough Set Model, The Mediterranean Journal on Computer and Networks 3(3): 72-79, 2007.
2. Han, J. Lin, T. Y., Li, J., Cercone, N., Constructing Associative Classifiers from Decision Tables, Lecture notes in computer science 4482: 305-313, Springer, 2007.
3. Lin, T. Y., Han, J., High Frequent Value Reduct in Very Large Databases, Lecture notes in computer science 4482: 346-354, Springer, 2007.
4. Han, J., Beheshti, M., Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases, Journal of Advanced Computational Intelligence and Intelligent Informatics 10(3):287-294, 2006.
5. Hu, X., Lin, T., Han, J., A New Rough Sets Model Based on Database Systems, Journal of Fundamenta Informaticae 59(2-3):135-152, 2004.
6. Han, J., Hu, X., Cercone, N., A visualization model of interactive knowledge discovery systems and its implementations, Journal of Information Visualization 2(2):105-125, 2003, Palgrave Macmillan.
7. Han, J. Using Table Lens to Interactively Build Classifiers, Applied Mathematics Letters 14:663-666, Elsevier Science Ltd., Pergamon, 2001.
8. Han, J., Cercone, N. Visualizing the Process of Knowledge Discovery, Journal of Electronic Imaging, SPIE, 9(4):404-420, 2000.
9. Dong, J., Han, J., Class and Object, Encyclopedia of Computer Science and Engineering, Wah, B. (Ed.), John Wiley & Sons, Inc., June 2006.
10. Han, J., R. Sanchez, Hu, X., Feature Selection Based on Relative Attribute Dependency: An Experimental Study, Lecture Notes in Computer Science 3641: 214-223, 2005.
11. Han, J., Hu, X., Lin, T., Feature Subset Selection Based on Relative Dependency between Attributes, Lecture Notes in Computer Science 3066:176-185, 2004.
12. Han, J., Hu, X., Lin, T., An Efficient Algorithm for Computing Core Attributes in Database Systems, Lecture Notes in Computer Science 2871: 663-667, 2003
13. Han, J., Hu, X., Lin, T., A New Computation Model for Rough Set Theory Based on Database Systems, Lecture Notes in Computer Science 2737: 381-390, 2003.
14. Hu, X., Lin T., Han, J., A New Rough Set Model Based on Database Systems, Lecture Notes in Computer Science 2639: 114-121, 2003.
15. Han, J., Cercone, N., Hu, X. An Interactive Visualization System for Mining Association Rules, in Data Mining, Rough Sets and Granular Computing, T. Lin, Y. Yao, L. Zadeh (eds.), 145-165, Physica-Verlag, 2002.
16. Hu, X., Cercone, N., Han, J., Ziarko, W., GRS: A Generalized Rough Sets Model, in Data Mining, Rough Sets and Granular Computing, by T. Lin, Y. Yao, and L. Zadeh (Eds.), 447-460, Physica-Verlag, 2002.
17. Han, J., Cercone, N., Interactive Construction of Classification Rules, Lecture Notes in Computer Science 2336: 529-534, 2002.
18. Han, J., Cercone, N., Implementation Issues and Paradigms of Visual KDD Systems, Lecture Notes in Computer Science 2198: 454-463, 2001.
19. Han, J., Cercone, N., Interactive Construction of Decision Trees, Lecture Notes in Computer Science 2035, 575-580, 2001.
20. Han, J., Hu, X., Cercone, N., Supervised Learning: A Generalized Rough Set Approach, Lecture Notes in Computer Science 2005:322-329, 2000.
21. Han, J., An, A., Cercone, N., CViz: An Interactive Visualization System for Rule Induction, Lecture Notes in Computer Science 1822:214-226, 2000.
22. Han, J., Cercone, N., Typical Example Selection for Learning Classifiers, Lecture Notes in Computer Science 1822:347-356, 2000.
23. Han, J., Cercone, N., AVIZ: A Visualization System for Discovering Numerical Association Rules, Lecture Notes in Computer Science 1805: 269-280, 2000.

Presentations:

1. Constructing Associative Classifiers from Decision Tables, The Conference on RSFDGrC, May 15, 2007, Toronto, Canada.

2. High Frequent Value Reduct in Very Large Databases, The Conference on RSFDGrC, May 17, 2007, Toronto, Canada.

3. Learning Fuzzy Association Rules and Associative Classification Rules, The IEEE World Congress on Computational Intelligence, July 19, 2006, Vancouver, Canada.

4. Mining Fuzzy Association Rules: Interestingness Measure and Algorithm, The 2nd IEEE International Conference on Granular Computing, May 12, 2006, Atlanta, Georgia, USA.

5. Anomaly Detection Based on a Rough Set Approach, The 4th Information Technology and Network Security Conference, March 23, 2006, Anaheim, California, USA.

6. Finding Protein Complexes from Online Biomedical Literature, in the computer science department colloquium, November 15, 2005.

7. How to Maintain Search Engine Repository Fresh, in the computer science department colloquium, Oct. 26, 2004.

8. Feature Selection Based on Relative Attribute Dependency: An Experimental Study, in the International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, September 1, 2005, Regina, Canada.

9. Feature Selection Based on Rough Set and Information Entropy, in the IEEE International Conference on Granular Computing, July 26, 2005, Beijing, China.

10. Integrating Relational Operations with Rough Set Theory to Select Feature Subsets, in the 9th World Multiconference on Systemics, Cybernetics and Informatics, July 12, 2005, Orlando, Florida, USA.

11. Extracting and Mining Protein-Protein Interaction Network from Biomedical Literature, Proc. of IEEE International Symposium on Computational Intelligence in Bioinformatics and Computational Biology, October 8, 2004, La Jolla, CA, USA.

12. A New Computation Model for Rough Set Theory Based on Database Systems, September 10, 2003, Prague, Czech.



 

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Last updated September 12, 2007 by mk