Mohammed Zaki

Mohammed Zaki is a Professor and Department Head of Computer Science at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. in Computer Science from the University of Rochester (1998) and dual B.S./M.S. degrees from Angelo State University and the University of Rochester. His research focuses on data mining, machine learning, and graph-based methods, with applications in bioinformatics, financial analytics, and personal health. He has authored over 300 publications and 6 patents, including the textbook Data Mining and Machine Learning (2nd Ed., Cambridge University Press, 2020).

Education: Ph.D. (1998) and M.S. (1995) from University of Rochester; B.S. (1993) in Computer Science and Mathematics from Angelo State University.

Research Interests: Zaki's work spans novel data mining techniques for graph-structured/textual data, scalable graph algorithms (e.g., GRAIL, IBMGenerator), and applications in finance, healthcare, and bioinformatics. He has pioneered methods for frequent pattern mining, sequence analysis, and knowledge graph construction.

Recognition: Recipient of NSF CAREER Award, DOE Early Career Award, and Fellowships from IEEE, ACM, AAAS, and SIAM. His research is supported by NSF, DARPA, NIH, DOE, and industry partners like IBM, Google, and NVIDIA.

Grants & Leadership: Leads the CRAFT Center for Financial Technology research and serves on ACM SIGKDD's Board of Directors. Has chaired major conferences including SIGKDD, CIKM, and IEEE BigData. Active in open-source projects, maintaining tools like IBMGenerator for synthetic data creation and GRAIL for graph reachability queries.

Labs/Teams: Directs research in RPI's Data Mining Lab, focusing on scalable algorithms and real-world applications. Collaborates with interdisciplinary teams on health informatics, financial systems analysis, and molecular graph learning.