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.; Ramamohanarao, K.; Guns, T. An Investigation into PredictionÂ�Â�Optimisation for the Knapsack Problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2019, Vol. 11494 LNCS, pp. 241-257. DOI: 10.1007/978-3-030-19212-9_16
  2. Rathore, P.; Ghafoori, Z.; Bezdek, JC.; Palaniswami, M.; Leckie, C. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data. IEEE Transactions on Cybernetics. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2019, Vol. 49, Issue 5, pp. 1629-1641. DOI: 10.1109/TCYB.2018.2806886
  3. Chan, CA.; Yan, M.; Gygax, AF.; 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 LTD. 2019, Vol. 105, pp. 536-549. DOI: 10.1016/j.trc.2019.06.008
  5. Zameni, M.; Ghafoori, Z.; Sadri, A.; Leckie, C.; Ramamohanarao, K. Change Point Detection for Streaming High-Dimensional Time Series. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2019, Vol. 11448 LNCS, pp. 515-519. DOI: 10.1007/978-3-030-18590-9_78
  6. Fahiman, F.; Disano, S.; Erfani, SM.; Mancarella, P.; Leckie, C. Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks. IEEE Transactions on Power Systems. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2019, Vol. 34, Issue 3, pp. 2281-2291. DOI: 10.1109/TPWRS.2018.2884711
  7. Weerasinghe, S.; Erfani, SM.; 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, pp. 469-472. DOI: 10.1109/LCN.2018.8638065
  8. Jia, Y.; Bailey, J.; Ramamohanarao, K.; Leckie, C.; Ma, X. Exploiting patterns to explain individual predictions. Knowledge and Information Systems. Springer Science and Business Media LLC. 2019. DOI: 10.1007/s10115-019-01368-9
  9. Jia, Y.; Bailey, J.; Ramamohanarao, K.; Leckie, C.; Houle, ME. Improving the quality of explanations with local embedding perturbations. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '19. ACM Press. 2019, pp. 875-884. DOI: 10.1145/3292500.3330930
  10. 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 LTD. 2019, Vol. 101, pp. 53-59. DOI: 10.1016/j.automatica.2018.11.048
  11. Yan, M.; Chan, CA.; Gygax, AF.; Yan, J.; Campbell, L.; Nirmalathas, A.; Leckie, C. Modeling the Total Energy Consumption of Mobile Network Services and Applications. Energies. MDPI. 2019, Vol. 12, Issue 1. DOI: 10.3390/en12010184
  12. Moshtaghi, M.; Bezdek, JC.; Erfani, SM.; Leckie, C.; Bailey, J. Online cluster validity indices for performance monitoring of streaming data clustering. International Journal of Intelligent Systems. WILEY. 2019, Vol. 34, Issue 4, pp. 541-563. DOI: 10.1002/int.22064
  13. 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. Institute of Electrical and Electronics Engineers (IEEE). 2019, Vol. 7, pp. 88504-88516. DOI: 10.1109/ACCESS.2019.2924928
  14. Doan, MT.; 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, pp. 106-111. DOI: 10.1109/BigData.2018.8622122
  15. Ganji, M.; Chan, J.; Stuckey, PJ.; Bailey, J.; Leckie, C.; Ramamohanarao, K.; Park, L. Semi-supervised blockmodelling with pairwise guidance. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing. 2019, Vol. 11052 LNAI, pp. 158-174. DOI: 10.1007/978-3-030-10928-8_10
  16. Weerasinghe, S.; Erfani, SM.; Alpcan, T.; Leckie, C. Support vector machines resilient against training data integrity attacks. Pattern Recognition. Elsevier BV. 2019, Vol. 96, pp. 106985-106985. DOI: 10.1016/j.patcog.2019.106985
  17. Lim, KH.; Chan, J.; Karunasekera, S.; Leckie, C. Tour recommendation and trip planning using location-based social media: a survey. Knowledge and Information Systems. Springer Verlag. 2019, Vol. 60, Issue 3, pp. 1247-1275. DOI: 10.1007/s10115-018-1297-4
  18. Zameni, M.; Sadri, A.; Ghafoori, Z.; Moshtaghi, M.; Salim, FD.; Leckie, C.; Ramamohanarao, K. Unsupervised online change point detection in high-dimensional time series. Knowledge and Information Systems. Springer Science and Business Media LLC. 2019. DOI: 10.1007/s10115-019-01366-x
  19. Zameni, M.; He, M.; Moshtaghi, M.; Ghafoori, Z.; Leckie, C.; Bezdek, JC.; Ramamohanarao, K. Urban sensing for anomalous event detection: Distinguishing between legitimate traffic changes and abnormal traffic variability. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing. 2019, Vol. 11053 LNAI, pp. 553-568. DOI: 10.1007/978-3-030-10997-4_34
  20. Leckie, C.; Tang, Z.; Kuijper, M.; Mareels, I. Attack correction for noise-free linear systems subject to sensor attacks. . Hong Kong University of Science and Technology. 2018, pp. 18-21.

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