Mohammed J. Zaki is a Professor of Computer Science at RPI. He received his Ph.D. degree in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining techniques, especially in bioinformatics. He has published over 200 papers and book-chapters on data mining and bioinformatics. We was the founding co-chair for the BIOKDD series of workshops. He is currently an Executive Editor for Statistical Analysis and Data Mining, and an Associate Editor for Data Mining and Knowledge Discovery, ACM Transactions on Knowledge Discovery from Data, Knowledge and Information Systems, ACM Transactions on Intelligent Systems and Technology, Social Networks and Mining, and International Journal of Knowledge Discovery in Bioinformatics. He was the program co-chair for SDM'08, SIGKDD'09 and PAKDD'10. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He also received the ACM Recognition of Service Award in 2003 & 2009, and an IEEE Certificate of Appreciation in 2005. He received the HP Labs Innovation Award in 2010. He is a Senior Member of IEEE, and was recently designated as an ACM Distinguished Scientist.
B.S., Computer Science and Mathematics (dual), May 1993, Angelo State University, San Angelo, Texas M.S., Computer Science, May 1995, University of Rochester, Rochester, New York Ph.D., Computer Science, July 1998, University of Rochester, Rochester, New York
Focus AreaData Mining, Bioinformatics
Selected Scholarly WorksMohammed J. Zaki, Naren Ramakrishnan, Lizhuang Zhao, Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling, International Journal of Knowledge Discovery in Bioinformatics, Jason T.L. Wang (ed.), 2010 (accepted, to appear)
Hilmi Yildirim, Vineet Chaoji, Mohammed J. Zaki, GRAIL: Scalable Reachability Index for Large Graphs, Proceedings of the VLDB Endowment, Vol3 ̇, No1 ̇, pp mm-nn, 2010 (Proceed- ings of the 36th International Conference on Very Large Data Bases, Singapore, September 2010).
Karam Gouda, Mosab Hassaan, Mohammed J. Zaki, PRISM: An Effective Approach for Frequent Sequence Mining via Prime-Block Encoding, Journal of Computer and Systems Sciences, special issue on Intelligent Data Analysis, Radim Belohlavek and Rudolph Kruse (eds.), Vol. 76, No. 1, pp 88-102, February 2010.
Saeed Salem, Mohammed J. Zaki and Chris Bystroff, FlexSnap: Flexible Non-Sequential Pro- tein Structure Alignment, Algorithms in Molecular Biology, Vol. 5, Article 12, 2010.
Mohammed J. Zaki, Christopher D. Carothers, and Boleslaw K. Szymanski, VOGUE: A Vari- able Order Hidden Markov Model with Duration based on Frequent Sequence Mining, ACM Transactions on Knowledge Discovery in Data, Vol. 4, No. 1, Article 5, January 2010.
Vineet Chaoji, Mohammad Hasan, Saeed Salem, and Mohammed J. Zaki, SPARCL: An Ef- fective and Efficient Algorithm for Mining Arbitrary Shape-based Clusters, Knowledge and Information Systems, invited as one of the best papers of IEEE Int’l Conference on Data Mining (ICDM’08), Vol. 21, No. 2, pp 201-229, November 2009.
Mohammad Al Hasan, Mohammed J. Zaki, Output Space Sampling for Graph Patterns, Pro- ceedings of the VLDB Endowment, Vol2 ̇, No1 ̇, pp 730-741, 2009 (Proceedings of the 35th International Conference on Very Large Data Bases, Lyon, France, August 2009).
Saeed Salem, Mohammed J. Zaki and Chris Bystroff, Iterative Non-Sequential Protein Struc- tural Alignment, Journal of Bioinformatics and Computational Biology, special issue on the best of CSB’08, Ying Xu and Peter Markstein (eds.), Vol. 7, No. 3, pp 571-596, June 2009.
Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Mohammed J. Zaki, An integrated, generic approach to pattern mining: data mining template library, Data Mining and Knowledge Discovery, Vol. 17, No. 3, pp. 457-495, December 2008.
Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Jeremy Besson, Mohammed J. Zaki, ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Pat- terns, Statistical Analysis and Data Mining, Vol. 1, Issue 2, pp. 67-84, (DOI: 10.1002/sam.10004) June 2008.