My Expertise
Artificial Intelligence, Machine Learning, Swarm Intelligence, Swarm Robotics, Trusted Autonomy, and Computational Linguistics.
Keywords
Fields of Research (FoR)
Artificial intelligence, Machine learning, Neural networks, Computational linguistics, Intelligent robotics, Autonomous agents and multiagent systems, Data engineering and data science, Deep learning, Semi- and unsupervised learning, Machine learning not elsewhere classifiedBiography
Dr Heba El-Fiqi is a Senior Lecturer in Artificial Intelligence with the School of Systems and Computing (SYSCOM) at UNSW Canberra. Her research focuses on decentralised intelligent systems, integrating swarm intelligence, representation learning, and cognitive signal processing to develop robust and scalable AI for complex environments. She has published in leading international venues, including IEEE Transactions on Cybernetics, IEEE...view more
Dr Heba El-Fiqi is a Senior Lecturer in Artificial Intelligence with the School of Systems and Computing (SYSCOM) at UNSW Canberra. Her research focuses on decentralised intelligent systems, integrating swarm intelligence, representation learning, and cognitive signal processing to develop robust and scalable AI for complex environments. She has published in leading international venues, including IEEE Transactions on Cybernetics, IEEE Transactions on Information Forensics and Security, and IEEE Access, and has built a strong research profile supported by more than AUD $1.2 million in competitive funding, including $1 million in external grants.
Her technical contributions include the development of the Weighted Gate Layer Autoencoder (WGLAE), a neural architecture that incorporates learnable gating mechanisms to support robust feature learning and signal recovery. WGLAE has been used as a benchmark in EEG signal processing and cognitive biometric research, particularly for reconstructing missing data in multivariate time-series settings. She also developed the open-source Shepherding Library for Swarm Guidance, which enables the research community to model and evaluate context-aware behaviours in decentralised multi-agent systems.
Dr El-Fiqi served as the AI Discipline Coordinator at SEIT (2021–2023) and has played a central role in curriculum and course development. She led the development of ZEIT4150 (Fundamentals of AI, UG, 2022) and ZEIT8601 (Applied Machine Learning, PG, 2024), and co-designed ZEIT4151 (Machine Learning, UG, 2022). Her teaching is consistently highly rated, with student satisfaction in these courses typically ranging from 90% to 100%.
She serves as an Academic Editor for PLOS ONE, is a Senior Member of IEEE, and contributes actively to the IEEE Computational Intelligence Society, including as Vice-Chair of the IEEE CIS Mentoring Committee and Co-Lead of the IJCNN CIS Mentoring Program (2024–2025). Her professional service also includes leadership and organising roles in equity and inclusion initiatives, including co-chairing Women in AI activities for IEEE SSCI 2020 and Canberra AI Week 2020, contributing to IEEE CIS Diversity and Inclusion and Mentoring subcommittees (2022–2024), and supporting women’s advancement through programs such as the WOMEN@UNSW Canberra Champions initiative (2022–2023). She has also contributed to outreach through initiatives such as YoWIE, including delivery of the Robotics stream in 2023 and 2024 and leadership of the stream in 2025.
My Qualifications
PhD in Computer Science (2009–2013)
- From: University of New South Wales UNSW (Australia), School of Engineering and Information Technology
- Thesis title: "Detection of Translator Stylometry using Pair-wise Comparative Classification and Network Motif Mining",
- Research Areas: Artificial Intelligence, Machine Learning, Social Network Analysis, Computational linguistics, Translator Stylometry, Natural Language Processing.
Masters of in Computer Science (2005–2009)
- From: Cairo University (Egypt), Faculty of Computers and Information
- Thesis title: "An Intelligent System for Tracking Network Attacks"
- Research Areas: Artificial Intelligence, Artificial Neural Network, Network Attacks, Internet Worms, Detecting Unknown Viruses.
My Awards
Awards
- Dell EMC Award of Distinction (2017): Achievement: delivering data analytics for big data and supporting students through an academic alliance program to obtaining EMC Data Scientist Associate (EMCDSA) certification with a success rate of 98% (317 certified students out of 323 enrolled students).
- IBM Big Data Developer - Instructor Award for Educators (2017).
Certificates
- May 1, 2019, Research to Impact Program Completion, Canberra Innovation Network.
- May 5, 2018, Foundations of University Learning and Teaching (FULT) Program Completion, UNSW.
- Feb 2, 2017, IBM Big Data Developer 2016, Mastery Award, IBM.
- Jul 21, 2016, Big Data Specialist with IBM BigInsights V2.1, IBM.
- May 11, 2016, EMC Academic Associate, Data Science and Big Data Analytics, Dell EMC.
- Oct 31, 2011, Graduate Teacher Training (GTTP) Program Completion, UNSW.
- Sep 3, 2008, Cisco Networking Academy Instructor, Cisco.
- Jul 28, 2007, Microsoft Certified Systems Engineer: Security (MCSE +Security), Microsoft.
- Jul 28, 2007, Microsoft Certified Systems Administrator: Security (MCSA +Security), Microsoft.
- Jul 28, 2007, Microsoft Certified Systems Engineer (MCSE), Microsoft.
- Jul 28, 2007, Microsoft Certified Systems Administrator (MCSA), Microsoft.
- Aug 12, 2006, Microsoft Certified Professional (MCP), Microsoft.
- Jul 27, 2006, Cisco Certified Network Associate (CCNA), Cisco.
My Research Activities
Dr Heba El-Fiqi’s research centres on decentralised intelligent systems methods that enable groups of agents to learn, coordinate, and adapt without relying on centralised control. Her work brings together swarm intelligence, representation learning, and cognitive signal processing, with the goal of building AI that remains effective under uncertainty, partial information, and dynamic operating conditions. Within this broader agenda, she has made sustained contributions to the design of algorithms and learning frameworks that support robust autonomy in complex environments.
A core component of Dr El-Fiqi’s research is swarm guidance and multi-agent coordination, including influential work on shepherding-based swarm control. To support both reproducibility and wider uptake of this research, she developed the open-source Shepherding Library for Swarm Guidance, a modular platform that enables researchers to model, test, and compare context-aware shepherding behaviours and heterogeneous agent interactions. By providing a reusable research infrastructure, this library strengthens the empirical foundation of swarm guidance research and helps translate conceptual models into evaluated, extensible implementations.
Alongside her decentralised autonomy work, Dr El-Fiqi has advanced learning methods for signal recovery and robust feature learning, particularly in settings where real-world data are incomplete or noisy. Her development of the Weighted Gate Layer Autoencoder (WGLAE) introduced learnable gating mechanisms designed to improve reconstruction and representation quality, and the approach has been used as a benchmark in domains such as EEG signal processing and cognitive biometrics, where missing values and multivariate time-series challenges are common. Her work is characterised by methodologically grounded research that has attracted competitive support and has been disseminated through high-quality international publication outlets.
My Research Supervision
Supervision keywords
Areas of supervision
Artificial Intelligence, Machine Learning, Neural Networks, Multi-agent Swarm Intelligence
Currently supervising
HDR Students
Completion:
- Noushin Amin, MPhil, 2025
- Play and Protect: Exploring Game-Based Learning for Cyber Safety in Primary Education
- Joint Supervisor with Hussein Abbass, Joint Primary Supervisor.
PhD – Currently Supervising
- Aisha Alabsi, PhD, 2025 T3
- An Improved Framework for Distributed Artificial Intelligence Learning
- Primary Supervisor, with Jiankun Hu, Secondary Supervisor.
- Khan Md Hasib, PhD, started 2025 T1
- AI-Assisted Diagnostic Framework for Multimorbidity
- Primary Supervisor, with Ripon Chakrabortty, Secondary Supervisor, and Haribondhu Sarma, Secondary Supervisor.
- Abdelaziz Mostafa, PhD, started 2024 T3
- Hybrid Deep Learning Model for Enhancing Disinformation Detection
- Secondary Supervisor with Alireza Abbasi, Primary Supervisor.
- Qianchu Li, PhD, 2024 T3
- Can Artificial Intelligence Improve Training of Unmanned Aerial Systems Operators?
- Joint Supervisor with Oleksandra Molloy, Joint Primary Supervisor, and Gary Eves, Secondary Supervisor.
- Noha Abuaesh, PhD, 2024 T2
- Knowledge Representation and Communication Between Swarm Agents in a Constrained Environment
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- Joint Primary Supervisor with Hussein Abbass, Joint Supervisor.
- Anirban Roy, PhD, 2023 T3 (Part-time)
- Collective Intelligence of Satellites Enabled Through Decentralised Learning
- Joint Supervisor with Melrose Brown, Joint Primary Supervisor, and Tim Lynar, Secondary Supervisor.
- Randall McCutcheon, PhD, 2020 T2 (Part-time)
- Test and Evaluation of Artificial Intelligence Systems
- Secondary Supervisor with Keith Joiner, Joint Primary Supervisor, Li Qiao, Joint Supervisor, and Matthew Garratt, Secondary Supervisor.
Undergraduate Students
Undergraduate- Honours Project Supervision
- Alimah Muhammad, Honours of Computing and Cyber Security, 2024
- Comparative Analysis of Machine Learning Algorithms for Anomaly Detection Task
- Principal Supervisor.
- Jonathan Zhou, 4th Year Engineering Project, 2023
- Adversarial Patrolling via Modified Shepherding
- Principal Supervisor with Aya Hussein, Co-supervisor.
- Joseph Thomas, 4th Year Engineering Project, 2023
- Multi-Agent Exploration and Task Allocation in Unknown and Constrained Environments
- Principal Supervisor with Aya Hussein, Co-supervisor.
- Rana U. Riaz, 4th Year Engineering Project (CDF), 2022
- Curriculum-based Deep Reinforcement Learning for Autonomous AlphaDogFight Air-to-Air Combat
- Co-supervisor with Aya Hussein, Principal Supervisor.
- Rana M. Hasan, 4th Year Engineering Project, 2021
- Deep Reinforcement Learning for Humanoid Robot Navigation in Unknown Environments
- Joint Supervisor with Aya Hussein.
- David King, 4th Year Engineering Project (CDF), 2021
- Autonomous Learning of Swarm Countermeasures: An Evolutionary Neural Network Approach
- Co-supervisor with Hussein Abbass, Principal Supervisor, and Aya Hussein, Co-supervisor.
- Simon Halabi, 4th Year Engineering Project, 2020
- Conversational AI Approach For Conversing Sheep States From Simulated Data
- Co-supervisor with Hussein Abbass, Principal Supervisor.
- Nadia Govier, Honours of Computing and Cyber Security, 2018
- An Algorithm for Intrinsically Motivated Agents to Simultaneously Learn Multiple Objectives
- Co-supervisor with Kathryn Kasmarik, Principal Supervisor.
Undergraduate- (CDF) Engineering Research Project Supervision.
- Chris Wise, S1 2022, Developing Decentralised Resilience to Malicious Influence in Collective Perception Problem, Co-Supervisor with Aya Hussein Primary Supervisor.
- Rana Usama S2, 2021, Autonomous Navigation of Humanoid Robot Using Deep Reinforcement Learning-based Visual SLAM, Joint Supervisor with Aya Hussein.
- Viet Hoang Le, S2 2021, Analysis of different tracking algorithms for four-class object tracking on limited hardware robot, Joint Supervisor with Jo Plested.
- Rana Usama Riaz, S1 2021, Application of Reinforcement Learning Based Shepherding System on Diddyborg UGV, Joint Supervisor with Aya Hussein.
- Viet Hoang Le, S1 2021, Application of Deep Learning based Object Recognition on UGV Robots, Joint Supervisor with Jo Plested.
My Teaching
Artificial Intelligence Courses
- ZEIT4150 – Fundamentals of Artificial Intelligence (UG) (Course Convenor)
- 2022 S1
- 2023 S1
- 2024 S1
- 2025 S1
- ZEIT4151 – Machine Learning (UG)
- 2022 S2
- 2023 S1
- 2024 S2
- 2025 S2
- ZEIT8601 – Applied Machine Learning (PG) (Course Convenor)
- 2024 S2
- 2025 S2
Generic Computing Courses
- ZPEM1307 – Computational Problem Solving (UG)
- 2024 S1
- ZEIT1301 – IT Project 2 (UG)
- 2025 S2
Contact
Publications
ORCID as entered in ROS
https://orcid.org/0000-0001-8146-0110