Mohammed J. Zaki

Mohammed J. Zaki is a Professor of Computer Science at Rensselaer Polytechnic Institute, Troy, New York. 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. He is the founding co-chair for the BIOKDD series of workshops. He is currently Area 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 is a senior member of the IEEE, and was named an ACM Distinguished Scientist in 2010.

Publications

Foreword
Foreword
Mohammed J. Zaki. © 2012.
This Foreword is included in the book XML Data Mining: Models, Methods, and Applications.
Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling
Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling
Mohammed J. Zaki, Naren Ramakrishnan, Lizhuang Zhao. © 2012. 29 pages.
Regulatory network analysis and other bioinformatics tasks require the ability to induce and represent arbitrary boolean expressions from data sources. In this paper, the authors...
Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling
Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling
Mohammed J. Zaki, Naren Ramakrishnan, Lizhuang Zhao. © 2010. 29 pages.
Regulatory network analysis and other bioinformatics tasks require the ability to induce and represent arbitrary boolean expressions from data sources. In this paper, the authors...
Exploring Similarities Across High-Dimensional Datasets
Exploring Similarities Across High-Dimensional Datasets
Karlton Sequeira, Mohammed J. Zaki. © 2007. 32 pages.
Very often, related data may be collected by a number of sources, which may be unable to share their entire datasets for reasons like confidentiality agreements, dataset size...