Professor Christopher Leckie

  • Room: Level: 07 Room: 7.11
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

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

Personal webpage


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.

Recent publications

  1. Kumar D, Bezdek J, Rajasegarar S, Leckie C, Palaniswami M. A visual-numeric approach to clustering and anomaly detection for trajectory data. VISUAL COMPUTER. Springer. 2017, Vol. 33, Issue 3.
  2. Iredale TB, Monazam Erfani S, Leckie C. An efficient visual assessment of cluster tendency tool for large-scale time series data sets. IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers. 2017.
  3. Ristanoski G, Soni R, Rajasegarar S, Bailey J, Leckie C. Clustering aided support vector machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10358 LNAI.
  4. Liu L, Kan A, Leckie C, Hodgkin P. Comparative evaluation of performance measures for shading correction in time-lapse fluorescence microscopy. JOURNAL OF MICROSCOPY. Blackwell Science. 2017, Vol. 266, Issue 1.
  5. Moshtaghi M, Monazam Erfani S, Leckie C, Bezdek J. Exponentially Weighted Ellipsoidal Model for Anomaly Detection. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. John Wiley & Sons. 2017, Vol. 32, Issue 9.
  6. Salehi M, Leckie C, Bezdek J, Vaithianathan T, Zhang X. Fast Memory Efficient Local Outlier Detection in Data Streams (Extended Abstract). 2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017). IEEE Computer Society. 2017.
  7. Monazam Erfani S, Baktashmotlagh M, Moshtaghi M, Nguyen V, Leckie C, Bailey J, Kotagiri R. From shared subspaces to shared landmarks: A robust multi-source classification approach. 31st AAAI Conference on Artificial Intelligence (AAAI). 2017.
  8. Fahiman F, Bezdek J, Monazam Erfani S, Palaniswami M, Leckie C. Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms. IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers. 2017.
  9. Fahiman F, Monazam Erfani S, Rajasegarar S, Palaniswami M, Leckie C. Improving load forecasting based on deep learning and K-shape clustering. Proceedings of the International Joint Conference on Neural Networks. 2017, Vol. 2017-May.
  10. Zhang X, Dou W, He Q, Zhou R, Leckie C, Kotagiri R, Salcic Z. LSHiForest: A Generic Framework for Fast Tree Isolation based Ensemble Anomaly Analysis. 2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017). IEEE Computer Society. 2017.
  11. Calheiros RN, Ramamohanarao K, Buyya R, Leckie C, Versteeg S. On the effectiveness of isolation-based anomaly detection in cloud data centers. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE. John Wiley & Sons. 2017, Vol. 29, Issue 18.
  12. Anwar T, Liu C, Vu HL, Leckie C. Partitioning road networks using density peak graphs: Efficiency vs. accuracy. INFORMATION SYSTEMS. Pergamon. 2017, Vol. 64.
  13. Lim K, Chan J, Karunasekera S, Leckie C. Personalized itinerary recommendation with queuing time awareness. SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017.
  14. Lim K, Chan J, Leckie C, Karunasekera S. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowledge and Information Systems. Springer London. 2017.
  15. Han Y, Chan J, Alpcan T, Leckie C. Using Virtual Machine Allocation Policies to Defend against Co-Resident Attacks in Cloud Computing. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING. IEEE Computer Society. 2017, Vol. 14, Issue 1.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile