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PROF Christopher Leckie


  • Data Mining, Network Intrusion Detection, Artificial Intelligence for Telecommunications, Bioinformatics



  • Chris Leckie is a Professor in the Department of Computing and Information Systems at The University of Melbourne.

    Research interests
    - Artificial Intelligence (AI)
    - telecommunications
    - machine learning, fault diagnosis, distributed systems and design automation 

    Prof Leckie has a strong interest in developing AI techniques for a variety of applications in telecommunications, such as network intrusion detection, network management, fault diagnosis and wireless sensor networks. He also has an interest in scalable data mining algorithms for tasks such as clustering and anomaly detection with applications in bioinformatics.



Selected publications


Investigator on

Additional Grant Information

  • 2011: S. Karunasekera, C. Leckie, Shyamali, M. Palaniswami, B. Moran and P. Farrell, 'High resolution monitoring of atmospheric pollutants to identify their impact on population health', Interdisciplinary Seed Grant, Institute for a Broadband Enabled Society (IBES), University of Melbourne, $40K



Education and training

  • PhD, Monash University 1993
  • BE, Monash University 1987
  • BSc, Monash University 1985

Awards and honors

  • Excellence in Teaching Award, Faculty of Engineering, 2005
  • Kelvin Medal for Teaching, Faculty of Engineering, 2005
  • Excellence in Teaching Award, Dept of Computer Science and Software Engineering, 2003



Available for supervision

  • Y

Supervision Statement

  • I am always interested in potential PhD students with strong computer science and electrical engineering backgrounds. My students usually work on topics related to data mining and scalable/distributed machine learning, with specific application to problems in network intrusion detection, wireless sensor networks, bioinformatics, biomedical imaging and signal processing.