Sarah Erfani
Senior Lecturer (equiv US Assistant Professor)
School of Computing and Information Systems
Melbourne School of Engineering
The University of Melbourne
Contact
Email: [firstname].[lastname]@unimelb.edu.au
Location: Level 07, Room 7.16,
Doug McDonell Building, The University of Melbourne,
Parkville, VIC 3010, Australia
Phone: 03 90358156
Research Interests
- Machine Learning
- Artificial Intelligence
- Large-Scale Data Mining
- Time Series Analysis
- Cyber Security
- Data Privacy
Publications
2020
- Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie, “Invertible Generative Modeling using Linear Rational Splines”, International Conference on Artificial Intelligence and Statistics (AISTATS) , p. 17, 2019. pdf code
- Namrata Srivastava, Sadia Nawaz, Jason Lodge, Eduardo Velloso, Sarah Erfani, James Bailey, “Exploring the Usage of Thermal Imaging for Understanding Video Lecture Designs and Students' Experiences”, International Learning Analytics and Knowledge, 2020.
2019
- Li Li, Sarah Erfani, Chien Chan, Christopher Leckie, “Multi-scale trajectory clustering to identify corridors in mobile networks”, ACM International Conference on Information and Knowledge Management (CIKM), p. 4, 2019. pdf
- Sandamal Weerasinghe, Sarah Erfani, Tansu Alpcan, Christopher Leckie, “Support Vector Machines Resilient Against Training Data Integrity Attacks”, Pattern Recognition, 2019. pdf
- Zahra Ghafoori, Sarah Erfani, James Bezdek, Shanika Karunasekera, Christopher Leckie, “LN-SNE: Log-Normal Distributed Stochastic Neighbor Embedding for Anomaly Detection”, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019. pdf
- Florin Schimbinschi, Sarah Erfani, James Bailey, Christian Walder, “SynthNet: Learning to Synthesize Music End-to-End”, International Joint Conferences on Artificial Intelligence (IJCAI), p. 7, 2019. pdf
- Namrata Srivastava, Eduardo Velloso, Sarah Erfani, James Bailey, “Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-Report”, Conference on Human Factors in Computing Systems (CHI), 2019. pdf
- Yixin Su, Sarah Erfani, Rui Zhang, “MMF: Attribute Interpretable Collaborative Filtering”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2019. pdf
- Fateme Fahiman, Sarah Erfani, Christopher Leckie, “ Robust and Accurate Short-Term Load Forecasting: A Cluster Oriented Ensemble Learning Approach”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2019. pdf
- Li Li, Sarah Erfani, Chien Chan, Christopher Leckie, “Adaptive Edge Caching based on Popularity and Prediction for Mobile Networks”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2019. pdf
- Meng Yang, Sutharshan Rajasegarar, Sarah Erfani, Christopher Leckie, “Deep Learning and One-class SVM based Anomalous Crowd Detection”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2019. pdf
- Fateme Fahiman, Steven Disano, Sarah Erfani, Pierluigi Mancarella, Christopher Leckie, “Data Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks”, IEEE Transactions on Power Systems, 2019. pdf
2018
- Shuo Zhou, Sarah Erfani, James Bailey, “Online CP Decomposition for Sparse Tensors”, IEEE International Conference on Data Mining (ICDM), 2018. pdf
- Masud Moshtaghi, James Bezdek, Sarah Erfani, Christopher Leckie, James Bailey, “Online Cluster Validity Indices for Streaming Data”, International Journal of Intelligent Systems (IJIS), 2018. pdf
- Yi Han, Benjamin Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie and Paul Montague, “Reinforcement Learning for Autonomous Defence in Software-Defined Networking”, International Conference on Decision and Game Theory for Security (GameSec), 2018. pdf
- Sandamal Weerasinghe, Tansu Alpcan, Sarah Erfani, Christopher Leckie, Peyam Pourbeik and Jack Riddle, “Deep Learning Based Game-Theoretical Approach to Evade Jamming Attacks”, International Conference on Decision and Game Theory for Security (GameSec), 2018. pdf
- Yisen Wang, Bo Dai, Lingkai Kong, Hongyuan Zha, Xingjun Ma, Sarah Erfani, James Bailey, Le Song, Shu-Tao Xia, “Learning Deep Hidden Nonlinear Dynamics from Aggregate Data”, Conference on Uncertainty in Artificial Intelligence (UAI), 2018. pdf
- Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, James Bailey. “Dimensionality-Driven Learning with Noisy Labels”, International Conference on Machine Learning (ICML), p. 10, 2018. pdf
- Weihao Cheng, Sarah M Erfani, Rui Zhang, Kotagiri Ramamohanarao,“Predicting Complex Activities from Ongoing Multivariate Time Series”, International Joint Conference on Artificial Intelligence (IJCAI), p. 7, 2018. pdf
- Prameesha Sandamal Weerasinghe, Sarah Erfani, Tansu Alpcan, Christopher Leckie, “Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning”, IEEE Conference on Local Computer Networks (LCN), p. 4, 2018. pdf
- Prameesha Sandamal Weerasinghe, Tansu Alpcan, Sarah Erfani, Christopher Leckie, “Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines”, International Symposium on Mathematic Theory of Networks and Systems (MTNS), p. 4, 2018. pdf
- Xingjun Ma, Bo Li, Yisen Wang, Sarah Erfani, Sudanthi Wijewickrema, Michael Houle, Grant Schoenebeck, Dawn Song, James Bailey, “Charactrizing Adversarial Subspaces Using Local Intrinsic Dimensionality”, International Conference on Learning Representations (ICLR), p.15, 2018. pdf (Accepted in top 2%)
- Zahra Ghafoori, Sarah Erfani, Sutharshan Rajasegarar, James Bezdek, Shanika Karunasekera, Christopher Leckie, “Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018. pdf
- Weihao Cheng, Sarah M Erfani, Rui Zhang, Kotagiri Ramamohanarao,“Learning Datum-Wise Sampling Frequency for Energy-Efficient Human Activity Recognition”, Association for the Advancement of Artificial Intelligence (AAAI), p. 8, 2018. pdf
2017
- Shuo Zhou, Sarah Erfani, James Bailey, “SCED: A General Framework for Sparse Tensor Decompositions with Constraints and Elementwise Dynamic Learning”, IEEE International Conference on Data Mining (ICDM),p. 10, 2017. pdf
- Li Li, Sarah Erfani, and Christopher Andrew Leckie, “A Pattern Tree based Method for Mining Conditional Contrast Pattern from Multi-Source Data”, IEEE International Conference on Data Mining Workshops (ICDMW), p. 8, 2017. pdf
- Laurent Amsaleg, James Bailey, Sarah Erfani, Teddy Furon, Michael E Houle, Miloš Radovanovic, Nguyen Xuan Vinh, “The Vulnerability of Learning to Adversarial Perturbation Increases with Intrinsic Dimensionality”, IEEE Workshop on Information Forensics and Security (WIFS), 2017
- Elaheh Alipourchavary, Sarah Erfani, Christopher Leckie, “Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining”, ACM International Conference on Information and Knowledge Management (CIKM), p. 4, 2017. pdf
- Weihao Cheng, Sarah Erfani, Ruiand Zhang, Kotagiri Ramamohanarao, “Markov Dynamic Subsequence Ensemble for Energy-Efficient Activity Recognition”, International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), p. 10, 2017. pdf
- Punit Rathore, James C. Bezdek, Sarah Erfani, Sutharshan Rajasegarar, Marimuthu Palaniswami, “Ensemble fuzzy clustering using random projections of high dimensional data”, IEEE Transactions on Fuzzy Systems (TFS), p. 15, 2017. pdf
- Weihao Cheng, Sarah M Erfani, Rui Zhang, Kotagiri Ramamohanarao,“Accurate Recognition of the Current Activity in the Presence of Multiple Activities”, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), p. 12, 2017. pdf
- Timothy B. Iredale, Sarah Erfani, Christopher Leckie, “An efficient visual assessment of cluster tendency tool for large-scale time series data sets”, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), p. 8, 2017. pdf
- Fateme Fahiman, James C. Bezdek, Sarah Erfani, Marimuthu Palaniswami, Christopher Leckie, “Fuzzy c-Shape: A New Algorithm for Clustering Finite Time Series Waveforms”, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), p. 8, 2017. pdf (Best paper award)
- Fateme Fahiman, Sarah Erfani, Sutharshan Rajasegarar, Marimuthu Palaniswami, Christopher Leckie, “Improving Load Forecasting Based on Deep Learning and K-shape Clustering”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2017. pdf
- Sarah Erfani, Mahsa Baktashmotlagh, Masud Moshtaghi, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao, “From shared subspaces to shared landmarks: a robust multi-source classification approach”, Association for the Advancement of Artificial Intelligence (AAAI), p. 7, 2017. pdf
- Masud Moshtaghi, Sarah Erfani, Christopher Leckie, James C. Bezdek, “Exponentially weighted ellipsoidal model for anomaly detection”, International Journal of Intelligent Systems (IJIS), 2017.
2016
- Laurent Amsaleg, James Bailey, Sarah Erfani, Teddy Furon, Michael E Houle, Miloš Radovanovic, Nguyen Xuan Vinh, “The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality”, NII Technical Report, p. 16, 2016. pdf
- Sarah Erfani, Mahsa Baktashmotlagh, Masud Moshtaghi, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao, “Robust Domain Generalisation by Enforcing Distribution Invariance”, International Joint Conference on Artificial Intelligence (IJCAI), p. 7, 2016. pdf
- Vinh Xuan Nguyen, Sarah Erfani, Sakrapee Paisitkriangkrai, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, “Training robust models using random projection”, International Conference on Pattern Recognition (ICPR), p. 6, 2016. pdf
- Sarah Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, Christopher Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning”, Pattern Recognition, p. 14, 2016. pdf (Best paper award)
- Zahra Ghafoori, Sarah Erfani,Sutharshan Rajasegarar, Shanika Karunasekera, Christopher Leckie, “Anomaly detection in non-stationary data: ensemble based self-adaptive OCSVM”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2016. pdf
- Sarah Erfani, Mahsa Baktashmotlagh, Sutharshan Rajasegarar, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao, “R1STM: One-class support tensor machine with randomised kernel”, SIAM International Conference in Data Mining (SDM), p. 9, 2016. pdf
- Timothy Glennan, Christopher Leckie, Sarah Erfani, "Improved classification of known and unknown network traffic flows using semi-supervised machine learning”, Australasian Conference on Information Security and Privacy (ACISP), p.8, 2016. pdf
- Zahra Ghafoori, Sutharshan Rajasegarar, Sarah Erfani, Shanika Karunasekera, Christopher Leckie, “Unsupervised parameter estimation for one-class support vector machines has been updated”, Pacific Asia Knowledge Discovery and Data Mining (PAKDD), p. 12, 2016. pdf
- Lingjuan Lyu, Yee Wei Law, Sarah Erfani, Christopher Leckie, Marimuthu Palaniswami, “An improved scheme for privacy-preserving collaborative anomaly detection”, IEEE PERCOM workshop on Security Privacy and Trust in IoT (SPT-IoT), p.6, 2016. pdf
Pre 2016
- Sarah Erfani, Mahsa Baktashmotlagh, Sutharshan Rajasegarar, Shanika Karunasekera, Christopher Leckie, "R1SVM: a randomised nonlinear approach to large-scale anomaly detection", Association for the Advancement of Artificial Intelligence (AAAI), 2015. pdf
- Sarah Erfani, Yee Wei Law, Shanika Karunasekera, Christopher Leckie, Marimuthu Palaniswami, "Privacy-preserving collaborative anomaly detection for participatory sensing", Pacific Asia Knowledge Discovery and Data Mining (PAKDD), 2014, pp. 581-593. pdf
- Sarah Erfani, Shanika Karunasekera, Cristopher Leckie, Udaya Parampalli, "Privacy-preserving data aggregation in participatory sensing networks", International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2013, pp. 165-170. pdf
- Sarah Erfani, Sutharshan Rajasegarar, Christopher Leckie. "An efficient approach to detecting concept-evolution in network data streams." Australasian Telecommunication Networks and Applications Conference (ATNAC), 2011, pp. 1-7. pdf
Preprints
- Sukarna Barua, Xingjun Ma, Sarah Erfani, Michael E. Houle, James Bailey, “Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality”, p. 31, 2019. pdf
- Sukarna Barua, Sarah Erfani, James Bailey, “FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs”, p. 11, 2019. pdf
- Tansu Alpcan, Sarah Erfani, Christopher Leckie, “Toward the Starting Line: A Systems Engineering Approach to Strong AI”, p. 11, 2017. pdf
Recent Research Fundings
- Learning the Focus of Attention to Detect Distributed Coordinated Attacks, ARC DP, 2020–2022, $447k.
- Towards Robust Learning Systems via Amortised Optimisation and Domain Adaptation, Defence Science & Technology Group and Data61 CRP, 2019, $187k.
- Aligning Consent with Privacy Promises of Data Sharing Platforms, Network Society Institute Seed Funding, 2019, $28k.
- Situation-Aware Cognitive Radio Network using Machine and Deep Learning, Northrop Grumman, 2017–2018, $165k.
- Detection of Infected Internet-of-Thing (IoT) Devices to Prevent Distributed Denial of Service (DDoS) Attacks, Oceania Cyber Security Centre (OCSC) Proof of Concept Grant, 2018, $77k.
- Adversarial Machine Learning for Cybersecurity, Defence Science & Technology Group and Data61 CRP, 2017–2019, $705k.
- Cyber security for the Internet of Things (IoT), Early Career Research (ECR) Grant, 2017, $40k.
Current PhD Students
- Yujing Jiang
- Hadi Mohaghegh Dolatabadi
- Shiquan Yang
- Ifrah Saeed
- Yixin Su
- Jiabo He
- Elaheh Alipourchavary
- Namrata Srivastava
PhD Students – Completed
- Prameesha Weerasinghe
- Li Li
- Fateme Fahiman
- Sukarna Barua (MPhil)
- Weihao Cheng
- Florin Schimbinschi
- Shuo Zhou
Teaching
Projects
- Centre of Excellence for Automated Decision-Making and Society (ADM+S).
- Centre of Cyber Security Excellence (ACCSE). here
- Network Security and Analytics. here
- Adversarial Machine Learning (AML). here
- Aligning Content with Privacy Promises. here
- Smart City and Internet of Things (IoT). here
Awards and Distinctions
- Research Excellence Award: Melbourne School of Engineering Early Career Research Excellence, 2019.
- Best Paper Award: Fateme Fahiman, James C. Bezdek, Sarah Erfani, Marimuthu Palaniswami, Christopher Leckie, “Fuzzy c-Shape: A New Algorithm for Clustering Finite Time Series Waveforms”, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), p. 8, 2017.
- Best Paper Award: Sarah Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, Christopher Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning”, Pattern Recognition, p. 14, 2016.
- First prize in InfraHack competition, Melbourne infrastructure hack for Australia, 2014. here