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

http://scholar.google.com/citations?user=wUsI0cAAAAAJ

Biography

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. DemiroviĆ E, Stuckey PJ, Bailey J, Chan J, Leckie C, Kotagiri R, Guns T. An Investigation into PredictionÂ�Â�Optimisation for the Knapsack Problem. 16th International Conference, CPAIOR 2019, Thessaloniki, Greece, June 4–7, 2019, Proceedings. Springer Verlag. 2019, Vol. 11494 LNCS. DOI: 10.1007/978-3-030-19212-9_16
  2. Rathore P, Ghafoori Z, Bezdek J, Palaniswami M, Leckie C. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data. IEEE TRANSACTIONS ON CYBERNETICS. Institute of Electrical and Electronics Engineers. 2019, Vol. 49, Issue 5. DOI: 10.1109/TCYB.2018.2806886
  3. Chan C, Yan M, Gygax A, Li W, Li L, Chih-Lin I, Yan J, Leckie C. Big Data Driven Predictive Caching at the Wireless Edge. 2019 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE. 2019. DOI: 10.1109/ICCW.2019.8756663
  4. Ma J, Chan J, Ristanoski G, Rajasegarar S, Leckie C. Bus travel time prediction with real-time traffic information. Transportation Research Part C: Emerging Technologies. Pergamon-Elsevier Science. 2019, Vol. 105. DOI: 10.1016/j.trc.2019.06.008
  5. Zameni M, Ghafoori Z, Sadri A, Leckie C, Kotagiri R. Change Point Detection for Streaming High-Dimensional Time Series. DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Chiang Mai, Thailand, April 22–25, 2019, Proceedings. Springer Verlag. 2019, Vol. 11448 LNCS. DOI: 10.1007/978-3-030-18590-9_78
  6. Fahiman F, Disano S, Monazam Erfani S, Mancarella P, Leckie C. Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks. IEEE TRANSACTIONS ON POWER SYSTEMS. IEEE - Institute of Electrical and Electronic Engineers. 2019, Vol. 34, Issue 3. DOI: 10.1109/TPWRS.2018.2884711
  7. Kowsar Y, Velloso E, Kulik L, Leckie C. Demo: LiftSmart: A monitoring and warning wearable for weight trainers. the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 ACM International Symposium. 2019. DOI: 10.1145/3341162.3343795
  8. Weerasinghe P, Monazam Erfani S, Alpcan T, Leckie C, Riddle J. Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning. 2018 IEEE 43rd Conference on Local Computer Networks (LCN). IEEE. 2019, Vol. 2018-October. DOI: 10.1109/LCN.2018.8638065
  9. Jia Y, Bailey J, Kotagiri R, Leckie C, Ma X. Exploiting patterns to explain individual predictions. Knowledge and Information Systems. Springer London. 2019. DOI: 10.1007/s10115-019-01368-9
  10. Jia Y, Bailey J, Kotagiri R, Leckie C, Houle ME. Improving the Quality of Explanations with Local Embedding Perturbations. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Association for Computing Machinery Inc.. 2019. DOI: 10.1145/3292500.3330930
  11. Tang Z, Kuijper M, Chong MS, Mareels I, Leckie C. Linear system security-Detection and correction of adversarial sensor attacks in the noise-free case. AUTOMATICA. Pergamon-Elsevier Science. 2019, Vol. 101. DOI: 10.1016/j.automatica.2018.11.048
  12. Yan M, Chan C, Gygax A, Yan J, Campbell L, Nirmalathas A, Leckie C. Modeling the Total Energy Consumption of Mobile Network Services and Applications. ENERGIES. MDPIAG. 2019, Vol. 12, Issue 1. DOI: 10.3390/en12010184
  13. Moshtaghi M, Bezdek J, Monazam Erfani S, Leckie C, Bailey J. Online cluster validity indices for performance monitoring of streaming data clustering. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. John Wiley & Sons. 2019, Vol. 34, Issue 4. DOI: 10.1002/int.22064
  14. Jiang J, Versteeg S, Han J, Hossain MA, Schneider J-G, Leckie C, Farahmandpour Z. P-Gram: Positional N-Gram for the Clustering of Machine-Generated Messages. IEEE Access. 2019, Vol. 7. DOI: 10.1109/ACCESS.2019.2924928
  15. Doan M, Qi J, Rajasegarar S, Leckie C. Scalable Bottom-up Subspace Clustering using FP-Trees for High Dimensional Data. 2018 IEEE International Conference on Big Data (Big Data). IEEE. 2019. DOI: 10.1109/BigData.2018.8622122

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