Jiawei Han

Description

Address

Biography

Jiawei Han is a professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and an affiliate faculty member in the Beckman Institute Image Formation and Processing group. His recent research interests include data mining, data warehousing, stream data mining, spatiotemporal and multimedia data mining, biological data mining, social network analysis, text and Web mining, and software bug mining, with over 300 conference and journal publications. He has chaired or served in many program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), and International Conferences on Data Mining (2005 PC co-chair). He also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently serving as founding Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (TKDD), and on the Board of Directors for the Executive Committee of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). His book "Data Mining: Concepts and Techniques" (Morgan Kaufmann) has been used popularly as a textbook.

Honors

IEEE Computer Society Technical Achievement Award (2005); Three IBM Faculty Awards (2002-2005); ACM Fellow (2004); ACM SIGKDD Innovation Award (2004); The Outstanding Contribution Award at the 2002 International Conference on Data Mining; ACM Service Award (1999).

Research

  1. Mining Sequential and Structured Patterns: Scalability, Flexibility, Extensibility and Applicability (supported by NSF/IDM)
  2. Mining Dynamics of Data Streams in Multi-Dimensional Space (supported by NSF/IDM)
  3. Endowing Biological Databases with Analytical Power: Indexing, Querying, and Mining of Complex Biological Structures (supported by NSF/BIO)
  4. MotionEye: Querying and Mining Large Datasets of Moving Objects (supported by NSF/SEIII)
  5. Automatic On-the-fly Detection, Characterization, Recovery, and Correction of Software Bugs in Production Runs (supported by NSF/ITR)

Publications

  • 2008
    • Rajagopalan, J.; Han, J. H.; Saif, M. T. A., Bauschinger effect in unpassivated freestanding nanoscale metal films. Scripta Materialia 2008, 59, (7), 734-737.

    • Rajagopalan, J.; Han, J. H.; Salf, M. T. A., On plastic strain recovery in freestanding nanocrystalline metal thin films. Scripta Materialia 2008, 59, (9), 921-926.

  • 2006
    • Liu, C.; Fei, L.; Yan, X. F.; Han, J.; Midkiff, S. P., Statistical debugging: A hypothesis testing-based approach. IEEE Transactions on Software Engineering 2006, 32, (10), 831-848.

  • 2005
    • Xin, D.; Han, J.; Yan, X.; Cheng, H., Mining Compressed Frequent-Pattern Sets. Proceedings from the 2005 International Conference on Very Large Data Bases (VLDB'05), Trondheim, Norway, August 2005.

    • Yan, X.; Zhou, X.J.; Han, J., Mining Closed Relational Graphs with Connectivity Constraints. Proceedings from the 2005 International Conference on Knowledge Discovery and Data Mining (KDD'05), Chicago, IL, Aug. 2005.

    • Hu, H.; Yan, X.; Yu, P.; Han, J.; Zhou, X.J., Mining Coherent Dense Subgraphs across Massive Biological Networks for Functional Discovery. Proceedings from the 2005 International Conference on Intelligent Systems for Molecular Biology (ISMB 2005), Ann Arbor, MI, June 2005.

    • Yan, X., Yu, P., and Han, J. (2005), "Substructure Similarity Search in Graph Databases", Proceedings from the 2005 ACM-SIGMOD International Conference on Management of Data (SIGMOD'05), Baltimore, Maryland, June 2005.

    • Yan, X.; Yu, P.; Han, J., Graph Indexing Based on Discriminative Frequent Structure Analysis. ACM Transactions on Databased Systems, December 2005 (in print).

    • Aggarwal, C.; Han, J.; Wang, J.; Yu, P.S., On Efficient Algorithms for High Dimensional Projected Clustering of Data Streams. Data Mining and Knowledge Discovery 2005, 10, 251-272.

Press

The Communications Office maintains the information included in Beckman Institute's online directory listings. In order to update your directory listing, please submit the following information to directoryupdates@beckman.illinois.edu:

  • a short bio including information on your educational background and your field
  • any honors and awards you may have received
  • a description of your research (approximately 200-400 words)
  • a list of recent representative publications
  • a photo of yourself (you can submit one or we can take one for you)