CL

Christopher Leckie

University of Melbourne

Melbourne VIC, Australia
4.60/5 · 5 reviews

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5.008/20/2025

Encourages students to ask questions.

4.005/21/2025

Makes learning interactive and fun.

5.003/31/2025

Patient, kind, and always approachable.

4.002/27/2025

Makes learning engaging and enjoyable.

5.002/4/2025

Great Professor!

About Christopher

Christopher Leckie is a Professor in the School of Computing and Information Systems within the Faculty of Engineering and Information Technology at the University of Melbourne, a position he has held since his appointment in 2000. Prior to this, he served as a Principal Research Scientist at Telstra Research Laboratories. He obtained his BSc degree in 1985, BE degree in electrical and computer systems engineering with first class honours, and PhD degree in computer science in 1992, all from Monash University. Leckie is actively engaged in leading research initiatives, including as Theme Leader for AI Assurance in the university's AI Assurance Lab and as a Chief Investigator at the University of Melbourne node of the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S).

His research specializations center on artificial intelligence and machine learning, particularly robust and scalable algorithms for clustering and anomaly detection, with applications in cyber security, network management, fault diagnosis, telecommunications, and the Internet-of-Things, as well as adversarial machine learning. In recognition of his teaching excellence, he received the Faculty of Engineering Excellence in Teaching Award in 2004. Leckie has authored or co-authored over 380 scholarly works, with key publications including "High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning" (Pattern Recognition, 2016, cited over 1,500 times), "Survey of network-based defense mechanisms countering the DoS and DDoD problems" (ACM Computing Surveys, 2007, cited over 1,100 times), "Fuzzy c-means algorithms for very large data" (IEEE Transactions on Fuzzy Systems, 2012, cited over 600 times), and "Unsupervised anomaly detection in network intrusion detection using clusters" (2005, cited nearly 600 times). His research has amassed more than 19,000 citations on Google Scholar, underscoring his substantial influence in the fields of artificial intelligence, machine learning, anomaly detection, and cybersecurity.

Professional Email: caleckie@unimelb.edu.au