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.
- Artificial Intelligence (AI)
- 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.
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
Monash University 1993
Monash University 1987
Monash University 1985
Awards and honors
Excellence in Teaching Award, Faculty of Engineering,
Kelvin Medal for Teaching, Faculty of Engineering,
Excellence in Teaching Award, Dept of Computer Science and Software Engineering,
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.