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Siqi MaView profile

Senior Lecturer

• Automated Vulnerability Detection and Repair
• Mobile Security
• IoT Security
• +4 more

Dr. Siqi Ma is a Senior Lecturer at the UNSW Institute for Cyber Security (IFCYBER) within the School of Systems & Computing at the University of New South Wales (UNSW). He previously served as a Lecturer at the University of Queensland's School of Information Technology and Electrical Engineering (ITEE). He holds a Ph.D. in Information Systems from Singapore Management University (2018) and was a Postdoctoral Research Fellow at Data61, CSIRO. He also visited Carnegie Mellon University (CMU) in 2015. Current Role: Senior Lecturer, UNSW Institute for Cyber Security Former Role: Lecturer, University of Queensland Education: Ph.D. (Singapore Management University), Postdoc (Data61, CSIRO) His research spans automated vulnerability detection, mobile security, IoT security, network authentication, and graph-based adversarial robustness. Recent work focuses on drone configuration bugs, Android malware analysis via GNNs, federated learning privacy, and credential leakage in open-source projects. Key trends in his 2024-2025 publications include automated security analysis for embedded systems, deepfake detection in multimedia, and privacy-preserving mechanisms for distributed networks. He collaborates with institutions like Purdue University, Singapore Management University, and CSIRO Data61.

• Wireless Communication Systems
• Satellite Communication Networks
• Spectrum Sharing
• +3 more

Dr. Ying He is a Senior Lecturer at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). Her research focuses on wireless communication networks, particularly integrating machine learning with satellite and terrestrial systems. She holds a BEng from Beijing University of Posts and Telecommunications (2009) and a PhD from UTS (2017). Prior to her academic role, she worked on TD-LTE chip design at the Chinese Academy of Sciences. Affiliations : Faculty of Engineering and Information Technology Global Big Data Technologies Centre (GBDTC) Education : BEng in Telecommunications Engineering, Beijing University of Posts and Telecommunications (2009) PhD in Engineering (Telecommunications), UTS (2017) Her research interests include satellite communication (GEO-LEO integration), spectrum sharing, vehicular communication, and applying machine learning to physical layer algorithms. Notable contributions include optimizing beam design in LEO networks and developing secure IoT systems. She supervises PhD/Master’s students and teaches courses like CCNA and capstone projects. Funded projects span satellite networks, IoT security, and supply chain tracking. Recent grants include SmartSat CRC initiatives and collaborations with industry partners like Intel and Ericsson. Her work addresses challenges in 6G, UAV-enabled computing, and resilient quantum algorithms.

Yuan-Fang LiView profile

Associate Professor

• Knowledge Graphs
• Natural Language Processing
• Multimodality
• +5 more

Yuan-Fang Li is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He also serves as Associate Dean International. His research focuses on knowledge graphs, natural language processing, multimodality, and graph representation learning. He holds a PhD from National University of Singapore (2006) and a Bachelor of Computing (Honours) from the same institution (2002). Affiliations: Monash University (since 201?), National University of Singapore (PhD 2002-2006) Key Projects: Leading research on neuro-symbolic systems (HARNESS project), large-scale multimodal knowledge management, and maritime knowledge graphs Teaching: Taught courses including FIT4002, FIT4004, and supervised over 20 PhD students Research interests include complex question answering over knowledge graphs, knowledge extraction from text/images, and structural/temporal graph learning. He has published 152+ works with notable contributions to scene graph generation, event extraction, and LLM-based reasoning. Key awards include the 2020 Best Student Paper Award and 2017 Kurzweil Prize. Grants: ARC Discovery Projects, industry collaborations (e.g., Outotec Oy) Labs/Teams: Active in Monash's Data Science & AI research groups, leading neuro-symbolic AI initiatives

• Machine Learning
• Data Mining
• Computer Vision
• +3 more

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.

Marius PortmannView profile

Professor

• Cybersecurity
• Computer Networks
• Internet of Things (IoT)
• +3 more

Professor Marius Portmann is the UQ-Cisco Chair of Network Security at the School of Electrical Engineering and Computer Science (EECS), University of Queensland. His expertise spans Cybersecurity, IoT, and Applied AI. He holds a PhD from ETH Zurich (2003) and has led research in Software Defined Networking (SDN), blockchain, and energy-harvesting IoT systems. Education: PhD in Electrical Engineering from Swiss Federal Institute of Technology (ETH Zurich), 2003. Research focuses on securing IoT networks, AI-driven intrusion detection, and sustainable sensor systems. He has pioneered self-powered IoT systems using energy harvesters and developed frameworks like FlowTransformer for network analysis. His work bridges theoretical advancements with practical applications in smart tourism, energy efficiency, and edge computing. Recent publications highlight innovations in DDoS detection (P4-Secure), sensor-based environmental monitoring (EcoShower), and graph-based anomaly detection (XG-BoT). His datasets (e.g., NF-ToN-IoT-v3) are widely used in ML-based cybersecurity research. Collaborations include industry partners like Cisco and institutions like RMIT. Grants and leadership roles in interdisciplinary projects underscore his impact. He advises on IoT security standards and contributes to open-source tools for network research. Current projects explore edge-AI integration and sustainable sensor networks.

• Large deformation FE analysis of soils
• Soil–structure interaction
• Offshore foundation systems
• +7 more

Yuxia Hu is a Professor at the University of Western Australia, affiliated with the School of Engineering (Civil, Environmental and Mining Engineering) and the School of Social Sciences, Planning and Transport Research Centre. Her research focuses on geotechnical engineering, particularly in large deformation FE analysis, offshore foundation systems, and soil-structure interaction. She has contributed to advancements in suction caissons, plate anchors, and computational mechanics, with applications in offshore wind energy and infrastructure stability. Research Interests: Large deformation FE analysis of soils, soil-structure interaction, offshore foundation systems, soil mechanics, and pavement engineering. Awards: Telford Premium, British Geotechnical Association Prize, and Significant Junior/Senior Paper Award. Grants: Leads projects on offshore anchors, carbon capture in pavements, and road maintenance optimization. Her work addresses challenges in geotechnical design and sustainable infrastructure, with a focus on numerical modeling and experimental validation. Collaborations span academia and industry, emphasizing practical solutions for complex soil-structure systems.

• Ultra-low power systems
• Microelectronics
• Integrated Circuits
• +11 more

Professor Aniruddha Desai is a Research Professor and Director of the Centre for Technology Infusion (CTI) at La Trobe University. He holds a Bachelor’s in Industrial Electronics, a Master’s in Micro-electronics, and a PhD in Computer Science. His expertise spans microelectronics, AI, IoT, and sensor networks, with a focus on socially impactful applications like transportation, healthcare, and precision agriculture. Research Interests: Ultra-low power systems Micro-nano electronics AI/ML and edge computing IoT and sensor networks Transportation and logistics Major Projects: Led multi-million-dollar R&D programs in areas such as smart cities, energy management, and smart farming. Notable collaborations include the IIT Kanpur - La Trobe University Research Academy and the Asian Smart Cities Research and Innovation Network. Awards: Recipient of the 2016 Vice-Chancellor’s Award for Research Excellence and the 2020 Victorian Tall Poppy Award for Science. Served on advisory panels for the Australian Research Council and provided expert testimony in parliamentary inquiries. Labs/Teams: Directs the CTI, which delivers technology-based innovations to industry and government. Co-founded the Asian Smart Cities network to advance urban technology solutions.

Ediz CetinView profile

Associate Professor

• Radio Frequency Interference Detection
• Fault-Tolerant Reconfigurable Circuits
• Machine Learning Applications
• +4 more

Ediz Cetin is an Associate Professor in Digital Electronics Engineering at Macquarie University's School of Engineering and a member of the Astrophysics and Space Technologies Research Centre. He serves as Course Director for the MEng Electronics Engineering program and Chair of the School's Postgraduate Coursework Committee. His research focuses on radio frequency interference mitigation, fault-tolerant reconfigurable circuits for space applications, machine learning in RF signal analysis, and low-power digital circuit design. Education: PhD in Signal Processing (Unsupervised Adaptive Signal Processing Techniques for Wireless Receivers) B.Eng. (Hons.) in Control and Computer Engineering Research Interests: RF interference detection and localization GNSS anti-jamming and spoofing detection FPGA-based reconfigurable systems Space instrumentation and CubeSat technologies Machine learning for signal processing Awards: Excellence in Learning Innovation (FSE Teaching Award, 2022) Highly Commended Finalist – Vice-Chancellor’s Award for Learning Innovation (2022) Innovative Approaches – Highly Commended (FSE Teaching Award, 2020) Key Projects: SmartSat CRC (2020–2026): Smart Satellite Technologies and Analytics Spacecraft Innovation Lab (2021–2022) CubeSat Biological Payload (2019–2022) Teaching Contributions: Led the 'Improving Student Engagement with Anywhere and Any-time Laboratory Access' initiative (2019–2020), enhancing remote lab accessibility for students.

• Ocean Renewable Energy
• Robotics
• Biomechanics
• +2 more

Dr. Boyin Ding is an Associate Professor at the University of Adelaide , serving as Academic Director at Haide College and researcher in the Mechanical Engineering department within the Faculty of Sciences, Engineering and Technology. He leads the Wave Energy Research initiative established in 2014, while also contributing to Robotics and Biomechanics through his work with the Flinders Medical Device Research Institute. Research Areas: Ocean Wave Energy Harvesting Control Systems for Renewable Energy 6DOF Robotic Testing Spine Biomechanics Transnational Education Programs Key Collaborations: Australia-China Joint Research Centre for Offshore Wind & Wave Energy Acoustics, Vibration and Control Research Group Scientific Awards: Australian Endeavour Fellowship Malcolm Kinnaird Engineering Excellence Award (2012) His recent publications focus on hybrid offshore energy systems, nonlinear hydrodynamics in wave energy converters, and biomechanical testing technologies. He has developed control algorithms for floating offshore wind-wave systems and pioneered 6DOF robotic platforms for medical applications. As an eligible PhD supervisor, he actively collaborates with global industries and academic institutions.

• Lattice-Based Cryptography
• Post-Quantum Cryptography
• Cryptographic Protocols
• +5 more

Ron Steinfeld is an Associate Professor at the Cybersecurity Lab of the Faculty of Information Technology , Monash University . His research focuses on lattice-based cryptography , post-quantum cryptographic protocols , and privacy-preserving technologies . He has contributed to advancements in secure multiparty computation , digital signatures , and blockchain confidentiality . Key areas: Lattice-Based Cryptography, Zero-Knowledge Proofs, Blockchain Security Recent work trends: Quantum-safe protocols, Efficient sampling algorithms, Scalable blockchain solutions Scientific Awards : BEST PAPER AWARD (ASIACRYPT 2015) He has served on program committees for major conferences including CRYPTO , EUROCRYPT , and ASIACRYPT . Ron is a member of the Discrete Mathematics Research Group and has collaborated with institutions like Macquarie University in the past.

• Control Theory
• Advanced Control Systems
• Model Predictive Control
• +7 more

Liuping Wang is a Professor in the School of Electrical and Computer Engineering at RMIT University, Australia, since 2007. He serves as Head of Discipline for Electrical Energy and Control Systems since 2005 and teaches Advanced Control Systems (EEET 2100) and Real Time Estimation and Control (EEET 2221). Current academic rank: Professor Location: City Campus, Australia Industry collaborators: ANCA, Australian Power Academy, Advanced Manufacturing CRC His research interests span: Control Theory with applications to UAVs and industrial processes Development of Model Predictive Control systems System Identification using neural networks Robust Control for constrained systems Control of AC motors and power electronics Applications in biomedical research and food process monitoring The 15 most recent publications (2015-2025) demonstrate expertise in: UAV control systems with segmented surfaces Battery condition monitoring for electric vehicles Mult-agent robotics with coordination algorithms Smart grid security and electricity dispatch GPS-denied localization for mobile robots Disturbance observer control with input constraints As a supervisor, he oversees Masters Research and PhD projects but no specific student names are listed. His email is liuping.wang@rmit.edu.au for collaboration or supervision inquiries.

Patrick W. C. HoView profile

Lecturer

• Memristor
• Non-volatile memories
• VLSI
• +7 more

Dr. Patrick W. C. Ho is a Lecturer in the Department of Electrical & Computer Systems Engineering (ECSE) at Monash University Malaysia School of Engineering. He holds a PhD in Electronics Engineering from the University of Nottingham Malaysia Campus (2016), with research focusing on non-volatile FPGA architectures using memristors. His academic journey includes roles as a Scholarly Teaching Fellow and unit coordinator for courses like ECE2131 Electrical Circuits and ECE4063 Large Scale Digital Design. He has industry experience with Intel Microelectronics and Altera Corporation, alongside teaching A-level Physics at Methodist College Kuala Lumpur. Education: BEng (First Class Honours) in Engineering (2009) MSc in Science (2012) PhD in Electronics Engineering (2016) Research Interests: Dr. Ho specializes in memristor-based non-volatile memory systems, VLSI design, and FPGA architectures. His work bridges hardware design with emerging materials, as seen in his Q1 journal article on memristive LUTs. Collaborations with CAD-IT expand his focus into AI, image processing, and object recognition. Recent projects include studies on memristor substrate performance (2023–2026) and UAV communication reliability (2021–2024). Teaching and Industry Engagement: As ECSE’s Industrial Training Advisor and IAP representative, he actively connects academic curricula with industry needs. His teaching spans foundational engineering courses and advanced digital design modules. Labs and Collaborations: Active in CAD-IT partnerships for student FYP co-sponsorship. Research groups focus on nanotechnology, machine learning integration in UAV systems, and memristor material analysis.

Peter SuttonView profile

Associate Professor

• Engineering Education
• Embedded Computing Systems
• Reconfigurable Computing
• +3 more

Associate Professor Peter Sutton is an academic at the University of Queensland (UQ), holding roles as Deputy Head of School (Teaching and Learning) and Associate Professor in the School of Electrical Engineering and Computer Science. His research focuses on Engineering Education, Embedded Systems, Reconfigurable Computing, and Electronic Design Automation. He has contributed to curriculum design, remote lab management during the pandemic, and hardware-software co-design for embedded systems. Sutton completed his undergraduate studies at UQ and earned advanced degrees at Carnegie Mellon University, with over three decades of experience in computer systems research and education. Education Bachelor of Science, University of Queensland Bachelor (Honours) of Engineering, University of Queensland Masters of Science (Coursework), Carnegie Mellon University Doctor of Philosophy, Carnegie Mellon University Research Interests Sutton’s work spans engineering pedagogy, embedded system design, and reconfigurable computing. Recent projects include adapting hands-on labs for remote learning during the pandemic and optimizing FPGA-based architectures for data compression and encryption. His contributions to cache optimization and multiprocessor systems highlight his expertise in hardware-software integration. Publications His 50+ publications cover topics like FPGA implementations of neural networks, code compression techniques for VLIW processors, and embedded system design tools. Notable contributions include frameworks for reconfigurable system-on-chip development and methods to enhance debugging practices in post-novice students. Labs & Teams He collaborates within UQ’s School of Electrical Engineering and Computer Science, contributing to research groups focused on embedded systems and engineering education innovation.

• Antennas and Propagation
• Electromagnetics
• Wireless Networks
• +4 more

Can Ding is an Associate Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), affiliated with the Faculty of Engineering and IT. He is a core member of the Global Big Data Technologies Centre (GBDTC). Ding holds a BEng in Microelectronics from Xidian University (2009) and a joint PhD in Electromagnetic Fields and Microwave Technology from Xidian University and Macquarie University (2016). His research focuses on engineering electromagnetics, particularly in base station antennas for 5G/6G networks, cross-band interference mitigation, and antenna array design. He has led numerous industry-collaborative projects, including ARC-funded initiatives, and has over 120 publications in top-tier journals/conferences, featured in IEEE's 'What’s Hot in Antennas and Propagation.' Teaching highlights include coordinating foundational electrical engineering courses and studio subjects, recognized with UTS's 2023 'Mid-Career Educator of the Year' award. He actively contributes to professional societies like IEEE AP-S, serving as an editor and conference organizer. His awards include the ARC DECRA grant (2020), Top 2% World Scientist (2023), and multiple outstanding reviewer distinctions. His grants include 'Advancing Millimeter-Wave Base Station for 6G' (ARC DP, 2025–2027) and 'Maximizing 5G Signal Transparency' (UTS Bluesky, 2024). Ding supervises students in antenna design and has mentored over a dozen researchers, many of whom have won international conference awards.

• Structural Health Monitoring
• Civil Engineering
• IoT/Edge Computing
• +5 more

Dr. Zhen Peng is a Research Fellow at Curtin University's School of Civil and Mechanical Engineering, part of the Faculty of Science and Engineering. He holds an ARC Early Career Industry Fellowship (2025–2028), focusing on developing cost-effective bridge monitoring systems using computer vision and edge computing in collaboration with Main Roads WA. His work bridges structural engineering, IoT/edge computing, and machine learning to enhance infrastructure safety. Dr. Peng earned his PhD from Curtin University (Chancellor's Commendation, 2022). His research emphasizes structural dynamics, nonlinear damage detection, and mobile crowdsensing frameworks for infrastructure monitoring. He has published extensively in top journals like Engineering Structures and Structural Control and Health Monitoring , receiving notable awards such as the 2023 Best Paper Award and a Gold Medal in the China Postdoctoral Innovation Competition. His current projects include deploying IoT-driven systems for real-time bridge condition assessment and training students via available 2025 PhD scholarships. Dr. Peng teaches courses in civil engineering and structural analysis, contributing to both academia and industry through innovation in smart infrastructure technologies.