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 International Publishing. 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. 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. Elsevier BV. 2019, Vol. 105, pp. 536-549. DOI: 10.1016/j.trc.2019.06.008
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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.
  16. Yang, M.; Rashidi, L.; Rao, AS.; Rajasegarar, S.; Ganji, M.; Palaniswami, M.; Leckie, C. Cluster-based Crowd Movement Behavior Detection. 2018 Digital Image Computing: Techniques and Applications (DICTA). IEEE. 2018, pp. 346-353. DOI: 10.1109/DICTA.2018.8615809
  17. Yang, M.; Rashidi, L.; Rajasegarar, S.; Leckie, C.; Rao, AS.; Palaniswami, M. Crowd Activity Change Point Detection in Videos via Graph Stream Mining. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE. 2018, Vol. 2018-June, pp. 328-336. DOI: 10.1109/CVPRW.2018.00059
  18. Kumar, D.; Ghafoori, Z.; Bezdek, JC.; Leckie, C.; Ramamohanarao, K.; Palaniswami, M. Dealing with Inliers in Feature Vector Data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. WORLD SCIENTIFIC PUBL CO PTE LTD. 2018, Vol. 26, Issue Suppl.2, pp. 25-45. DOI: 10.1142/S021848851840010x
  19. Weerasinghe, S.; Alpcan, T.; Erfani, SM.; Leckie, C.; Pourbeik, P.; Riddle, J. Deep learning based game-theoretical approach to evade jamming attacks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing. 2018, Vol. 11199 LNCS, pp. 386-397. DOI: 10.1007/978-3-030-01554-1_22
  20. Zhang, X.; Salehi, M.; Leckie, C.; Luo, Y.; He, Q.; Zhou, R.; Kotagiri, R. Density biased sampling with locality sensitive hashing for outlier detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 11234 LNCS, pp. 269-284. DOI: 10.1007/978-3-030-02925-8_19

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