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 03, Room 3321, 

Melbourne Connect, The University of Melbourne, 

Carlton, VIC 3053, Australia 

Phone: 03 90358156

Research Interests 

• Machine Learning 
• Artificial Intelligence
• Large-Scale Data Mining 
• Time Series Analysis 
• Cyber Security 
• Data Privacy 

Publications 

2022

• Shiquan Yang, Sarah Erfani, Jey Han Lau, Rui Zhang “An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation”, Association for Computational Linguistics (ACL), p. 10, 2022. 

2021

• Hanxun Huang, Yisen Wang, Sarah Erfani, Quanquan Gu, James Bailey, Xingjun Ma, “Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks", Conference on Neural Information Processing Systems (NeurIPS), p. 19, 2021. 
• Jiabo He, Sarah Erfani, Xingjun Ma, James Bailey, Ying Chi3, Xian-Sheng Hua, “Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression", Conference on Neural Information Processing Systems (NeurIPS), p. 19, 2021. 
• Sandamal Weerasinghe, Tansu Alpcan, Sarah Erfani, Christopher Leckie, “Defending Support Vector Machines Against Data Poisoning Attacks", IEEE Transactions on Information Forensics and Security (TIFS), p. 14, 2021.
• Guohang Zeng, Yousef Kowsar, Sarah Erfani, James Bailey, “Generating Deep Networks Explanations with Robust Attribution Alignment", Asian Conference on Machine Learning (ACML), p. 16, 2021.
• Sandamal Weerasinghe, Ben Rubinstein, Sarah Erfani, Tansu Alpcan, Christopher Leckie, Tamas Abraham, “Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning”, International Joint Conferences on Artificial Intelligence (IJCAI), p. 10, 2021. pdf
• Shiquan Yang, Jey Han Lau, Sarah Erfani, Rui Zhang, “A Unified Framework to Incorporate Multimodal Knowledge Bases into End-to-End Task-Oriented Dialogue Systems", International Joint Conferences on Artificial Intelligence (IJCAI), p. 8, 2021. pdf
• Yixin Su, Rui Zhang, Sarah Erfani,  Junhao Gan, “Neural Graph Matching based Collaborative Filtering”, ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), p. 10, 2021. pdf
• Hanxun Huang, Xingjun Ma, Sarah Erfani, James Bailey, “Unlearnable Examples: Making Personal Data Unexploitable”, International Conference on Learning Representations (ICLR), p. 17, 2021. pdf
• Yixin Su, Rui Zhang, Sarah Erfani, Zhenghua Xu, “Detecting Beneficial Feature Interactions for Recommender Systems”, Association for the Advancement of Artificial Intelligence (AAAI), p. 9, 2021. pdf
• Namrata Srivastava, Sadia Nawaz, Joshua Newn, Jason Lodge, Eduardo Velloso, Sarah Erfani, Dragan Gasevic, James Bailey, “Are you with me? Measurement of Learners’ Video-Watching Attention with Eye Tracking”, International Learning Analytics and Knowledge (LAK), p. 11, 2021. pdf
• Yujing Jiang, Xingjun Ma, Sarah Erfani, James Bailey, “Dual Head Adversarial Training”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2021. pdf
• Hanxun Huang, Xingjun Ma, Sarah Erfani, James Bailey, “Neural Architecture Search via Combinatorial Multi-Armed Bandit”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2021. pdf
• Ifrah Saeed, Andrew C. Culleny, Sarah Erfaniy and Tansu Alpcan, “Domain-Aware Multiagent Reinforcement Learning in Navigation”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2021. pdf
• Elaheh Alipourchavary, Sarah Erfani, Christopher Leckie, “Mining Rare Recurring Events in Network Traffic using Second Order Contrast Patterns”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2021. pdf
• Farbod Taymouri, Marcello La Rosa, Sarah Erfani, “A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences”, SIAM International Conference in Data Mining (SDM), p. 9, 2021. pdf
• Qizhou Wang, Sarah Erfani, Christopher Leckie, Michael E. Houle, “A Dimensionality-Driven Approach for Unsupervised Out-of-distribution Detection”, SIAM International Conference in Data Mining (SDM), p. 9, 2021. pdf
  

2020

• Hadi M Dolatabadi, Sarah Erfani, Christopher Leckie, “AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows”, Conference on Neural Information Processing Systems (NeurIPS), p. 32, 2020. pdf
• Laurent Amsaleg, James Bailey, Amelie Barbe, Sarah Erfani, Teddy Furon, Michael Houle, Milos Radovanovic, Nguyen Xuan Vinh, “High Intrinsic Dimensionality Facilitates Adversarial Attack: Theoretical Evidence”,IEEE Transactions on Information Forensics and Security (TIFS), p. 12, 2020. pdf
• Shiquan Yang, Sarah Erfani and Rui Zhang “GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems”, Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 11, 2020. pdf
• Hadi M Dolatabadi, Sarah Erfani, Christopher Leckie, Black-box Adversarial Example Generation with Normalizing Flows, ICML workshop on Invertible Neural Networks and Normalizing Flows, p. 6, 2020. pdf
• Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, James Bailey, Normalized Loss Functions for Deep Learning with Noisy Labels, International Conference on Machine Learning (ICML), p. 11, 2020. pdf
• Zhiheng Zhong, Jiabo He, Maria A Rodriguez, Sarah Erfani, Ramamohanarao Kotagiri, Rajkumar Buyya, “Heterogeneous Task Co-location in Containerized Cloud Computing Environments”, 2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC), p. 12, 2020. pdf
• Farbod Taymouri, Marcello La Rosa, Sarah Erfani, Zahra Dasht Bozorgi, Ilya Verenich, “Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction”, International Conference on Business Process Management (BPM), p.16, 2020. pdf
• Jiabo He, Sarah Erfani, Sudanthi Wijewickrema, Stephen O'Leary and Kotagiri Ramamohanarao, “Learning Non-Unique Segmentation with Reward-Penalty Dice Loss”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2020. pdf
•  Jiabo He, Sarah Erfani, Sudanthi Wijewickrema, Stephen O'Leary and Kotagiri Ramamohanarao, “Segmented Pairwise Distance for Time Series with Large Discontinuities”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2020. pdf
• Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin Rubinstein, Christopher Leckie, Tansu Alpcan and Sarah Erfani, “Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2020. pdf
• Li Li, Sarah Erfani, Chien Chan and Christopher Leckie, “Discovery of contrast corridors from trajectory data in heterogeneous dynamic cellular networks”, International Joint Conference on Neural Networks (IJCNN), p. 8, 2020. pdf
• 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 (LAK), p. 10, 2020. pdf
  

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 

• Making Anomaly Detection Interpretable and Actionable in Hostile Environments, ARC DECRA, 2022—2024, $466K.
• Quantum Adversarial Machine Learning for Threat Identification and Mitigation, Army Quantum Technology Challenge, 2022, $50K.
• RAINBOW - RAdIo Networks Based On machine learning for situation aWareness, ARC Linkage Project, 2020–2023, $587k.
• Learning the Focus of Attention to Detect Distributed Coordinated Attacks, ARC Discovery Project, 2020–2022, $447k.
• Towards Robust Learning Systems via Amortised Optimisation and Domain Adaptation, Defence Science & Technology Group and Data61 CRP, 2019–2022, $312k.
• 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, 2017–2022, $1.014m.
• Cyber security for the Internet of Things (IoT), Early Career Research (ECR) Grant, 2017, $40k.

Current PhD Students

• Haitian He
• Chen Wang
• Shijie (Jason) Liu
• Hanxun (Curtis) Huang
• Yujing Jiang
• Hadi Mohaghegh Dolatabadi
• Shiquan Yang
• Ifrah Saeed
• Yixin Su
• Jiabo He

PhD Students – Completed 

• Elaheh Alipourchavary
• Guohang Zeng (MPhil)
• Namrata Srivastava
• Prameesha Weerasinghe
• Li Li
• Fateme Fahiman
• Sukarna Barua (MPhil)
• Weihao Cheng
• Florin Schimbinschi
• Shuo Zhou

Teaching 

• Security Analytics (COMP90073), 2019—2022
• Artificial Intelligence (COMP30024), 2017—2021
• Knowledge Technologies (COMP90049), 2017—2019

Projects 

• Centre of Excellence for Automated Decision-Making and Society (ADM+S). here
• 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: School of Computing and Information Systems Early Career Research Excellence, 2021.
• Finalist in Women in AI Awards (Cybersecurity), 2020.
• Finalist in Engagement Australia Excellence Awards, 2020.
• Research Excellence Award: Faculty of Engineering and IT 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