Dr Sarah Monazam Erfani

  • Room: Level: 07 Room: 16
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Big Data (Scalable Learning, Data Integration, Data Analysis)
  • Computer Security & Privacy (Cybersecurity, Data Privacy)
  • Data Mining
  • Machine Learning

Biography

Sarah Erfani is a lecturer in the School of Computing and Information Systems (CIS) at The University of Melbourne. Research interests:

  • Artificial Intelligence
  • Machine Learning
  • Cyber Security
  • Large-scale Data Mining
  • Data Privacy

Recent publications

  1. 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. 2018. DOI: 10.1109/TPWRS.2018.2884711
  2. 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). SpringerLink. 2018, Vol. 11199 LNCS, pp. 386-397. DOI: 10.1007/978-3-030-01554-1_22
  3. Ma, X.; Wang, Y.; Houle, ME.; Zhou, S.; Erfani, SM.; Xia, ST.; Wijewickrema, S.; Bailey, J. Dimensionality-Driven learning with noisy labels. 35th International Conference on Machine Learning, ICML 2018. JMLR. 2018, Vol. 8, pp. 5332-5341.
  4. Ghafoori, Z.; Erfani, SM.; Rajasegarar, S.; Bezdek, JC.; Karunasekera, S.; Leckie, C. Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines. IEEE Transactions on Neural Networks and Learning Systems. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2018, Vol. 29, Issue 10, pp. 5057-5070. DOI: 10.1109/TNNLS.2017.2785792
  5. Rathore, P.; Bezdek, JC.; Erfani, SM.; Rajasegarar, S.; Palaniswami, M. Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections. IEEE Transactions on Fuzzy Systems. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2018, Vol. 26, Issue 3, pp. 1510-1524. DOI: 10.1109/TFUZZ.2017.2729501
  6. Cheng, W.; Erfani, S.; Zhang, R.; Ramamohanarao, K. Learning datum-wise sampling frequency for energy-efficient human activity recognition. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 2018, pp. 2143-2150.
  7. Wang, Y.; Dai, B.; Kong, L.; Erfani, SM.; Bailey, J.; Zha, H. Learning deep hidden nonlinear dynamics from aggregate data. 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. 2018, Vol. 1, pp. 83-92.
  8. 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. 2018. DOI: 10.1002/int.22064
  9. Cheng, W.; Erfani, S.; Zhang, R.; Ramamohanarao, K. Predicting complex activities from ongoing multivariate time series. IJCAI International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence. 2018, Vol. 2018-July, pp. 3322-3328.
  10. Han, Y.; Rubinstein, BIP.; Abraham, T.; Alpcan, T.; De Vel, O.; Erfani, S.; Hubczenko, D.; Leckie, C.; Montague, P. Reinforcement learning for autonomous defence in software-defined networking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 11199 LNCS, pp. 145-165. DOI: 10.1007/978-3-030-01554-1_9
  11. Amsaleg, L.; Bailey, J.; Barbe, D.; Erfani, S.; Houle, ME.; Nguyen, V.; Radovanovic, M. The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality. 2017 IEEE Workshop on Information Forensics and Security, WIFS 2017. IEEE Explore. 2018, Vol. 2018-January, pp. 1-6. DOI: 10.1109/WIFS.2017.8267651
  12. Alpcan, T.; Weerasinghe, P.; Kuijper, M.; Monazam Erfani, S.; Leckie, C. Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines. . Hong Kong University of Science and Technology. 2018.
  13. Li, L.; Erfani, S.; Leckie, C. A Pattern Tree based Method for Mining Conditional Contrast Patterns of Multi-Source Data. IEEE International Conference on Data Mining Workshops, ICDMW. IEEE. 2017, Vol. 2017-November, pp. 916-923. DOI: 10.1109/ICDMW.2017.126
  14. Cheng, W.; Erfani, S.; Zhang, R.; Kotagiri, R. Accurate recognition of the current activity in the presence of multiple activities. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2017, Vol. 10235 LNAI, pp. 39-50. DOI: 10.1007/978-3-319-57529-2_4
  15. Iredale, TB.; Erfani, SM.; Leckie, C. An Efficient Visual Assessment of Cluster Tendency Tool for Large-scale Time Series Data Sets. IEEE International Conference on Fuzzy Systems. IEEE. 2017. DOI: 10.1109/FUZZ-IEEE.2017.8015587
  16. Moshtaghi, M.; Erfani, SM.; Leckie, C.; Bezdek, JC. Exponentially Weighted Ellipsoidal Model for Anomaly Detection. International Journal of Intelligent Systems. WILEY. 2017, Vol. 32, Issue 9, pp. 881-899. DOI: 10.1002/int.21875
  17. Erfani, SM.; Baktashmotlagh, M.; Moshtaghi, M.; Nguyen, V.; Leckie, C.; Bailey, J.; Ramamohanarao, K. From shared subspaces to shared landmarks: A robust multi-source classification approach. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI. 2017, pp. 1854-1860.
  18. Fahiman, F.; Bezdek, JC.; Erfani, SM.; Palaniswami, M.; Leckie, C. Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms. IEEE International Conference on Fuzzy Systems. IEEE. 2017. DOI: 10.1109/FUZZ-IEEE.2017.8015525
  19. Fahiman, F.; Erfani, SM.; 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. IEEE. 2017, Vol. 2017-May, pp. 4134-4141. DOI: 10.1109/IJCNN.2017.7966378
  20. Cheng, W.; Erfani, S.; Zhang, R.; Ramamohanarao, K. Markov dynamic subsequence ensemble for energy-efficient activity recognition. ACM International Conference Proceeding Series. Association for Computing Machinery (ACM). 2017, pp. 282-291. DOI: 10.1145/3144457.3144470

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