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Rachel Cardell-OliverView profile

Professor

• Sensor Networks
• Data Analytics
• Environmental Monitoring
• +7 more

Rachel Cardell-Oliver is a Professor and Head of Department in the Computer Science and Software Engineering department at the School of Physics, Maths and Computing , University of Western Australia (UWA). She is also affiliated with the UWA Data Institute and School of Social Sciences, Planning and Transport Research Centre. As President of the Computing Research and Education Association of Australasia (CORE), she leads national academic initiatives. In 2024, she became a Fellow of the Australian Computer Society . Her education includes a PhD in Computer Science (University of Cambridge) , and BSc and MSc (University of Western Australia) . She has held roles at the University of Essex, SRI Cambridge, and UWA. Her research focuses on sensor networks , distributed intelligence , and applications in environmental monitoring (e.g., soil moisture, rammed earth buildings), public transport systems, smart water meters, wastewater treatment, and privacy-preserving data analytics. Her work aligns with UN Sustainable Development Goals, particularly Education and Environment . Key achievements include pioneering smart metering systems and data-driven urban infrastructure solutions . She has authored over 90 publications and secured 28 grants, including major projects like the ARC Training Centre for Transforming Maintenance through Data Science . Grants & Projects : Smart water treatment, transport systems, stormwater management, and sensor network deployments. Labs/Teams : BuildSense (building monitoring), RailSmart (transport planning), and LoRa Underground Farm Sensor Network datasets.

• Construction Safety and Health
• Building Information Modelling (BIM)
• Construction Management
• +3 more

Carol Hon is an Associate Professor at the School of Architecture & Built Environment, Faculty of Engineering, Queensland University of Technology (QUT). Her research focuses on construction safety, mental health, and Building Information Modelling (BIM). Prior to academia, she worked as an Assistant Quantity Surveyor in Hong Kong. Education: PhD, Hong Kong Polytechnic University MPhil, Hong Kong Polytechnic University Bachelor of Science in Surveying, University of Hong Kong Graduate Certificate in Academic Practice, QUT Research Interests: Construction safety and health, BIM adoption, mental health in construction, gender diversity, and professional ethics. Key Publications: Over 50 publications in journals like Safety Science, ASCE Journals, and Engineering, Construction and Architectural Management. Recent works include studies on mental health scales for construction workers and safety communication barriers. Awards: Including two Hong Kong doctoral dissertation awards, an ARC DECRA, and best paper awards. Recognized for contributions to construction safety and mental health initiatives. Grants/Projects: Co-investigator on projects funded by the National Natural Science Foundation of China, Hong Kong Research Grants Council, and industry partners. Topics include organizational learning from accidents, electrical safety in RMAA works, and ethnic minority safety. Roles: Safety Theme Coordinator for Building 4.0 CRC, BrisBIM Committee member, and reviewer for top-tier construction journals.

• Cybersecurity
• Artificial Intelligence
• Smart Grids
• +5 more

Professor Naveen Chilamkurti is a leading academic at La Trobe University, serving as the Head of Cybersecurity Discipline and Director of the La Trobe Cybersecurity Research Hub. He holds a Professorship in the Department of Computer Science and Information Technology within the School of Computing, Engineering & Mathematical Sciences. His academic journey includes degrees from La Trobe University: BEng (BU), PGradDipCompSc, MCompSc, and PhD. His research focuses on cybersecurity, blockchain technology, smart grids, IoT, and AI applications in healthcare and smart cities. Key areas include secure energy trading frameworks, privacy-preserving machine learning, and distributed consensus algorithms. He explores interdisciplinary topics like AI in HR management, IoT-enabled healthcare systems, and resilient smart grid management. Recent publications highlight innovations in blockchain applications for SME financing, AI-driven threat detection, and sustainable energy systems. His work emphasizes privacy enhancement in federated learning, decentralized authentication mechanisms, and cybersecurity solutions for IoT networks. Collaborations span academia and industry, addressing real-world challenges in smart city infrastructure and cyber-physical systems. He leads the La Trobe Cybersecurity Research Hub, driving projects on ethical frameworks for critical technologies and AI in healthcare diagnostics. His contributions include frameworks like Hydrakon for deception detection and RACER for IoT optimization. While no specific awards are listed, his prolific publishing and leadership roles underscore his impactful contributions to computing and cybersecurity.

• Customer Analytics
• Social Media Analytics
• Churn Management
• +3 more

Ali Tamaddoni Jahromi is an Associate Professor in the Department of Marketing at Deakin Business School, part of Deakin University's Faculty of Business and Law. He holds a PhD from Monash University and a Graduate Certificate in Higher Education from Deakin University. His research focuses on customer analytics, social media analytics, churn management, and user-generated content (UGC), with publications in high-impact journals like the Journal of Marketing Research and Journal of Service Research . Dr. Tamaddoni has secured grants totaling over $38k, including a 2023 grant on AI alignment strategies and a 2022 project on churn management for Legal Super. He actively supervises doctoral and master's students on topics like consumer fake review perception, AI in e-commerce, and marketing budget allocation. His awards include the ANZMAC 2018 Best Reviewer Award and a 2008 Best Paper Award in Business-to-Business Marketing. He teaches advanced courses such as Marketing Analytics (MIS784, MIS384), Descriptive Analytics, and Business Analytics Project units. His research spans predictive modeling, customer retention strategies, and the impact of digital platforms on consumer behavior.

• Marketing Technology
• Customer Lifetime Value Modelling
• Market Response Modelling
• +6 more

Andre Bonfrer is a Professor of Marketing at Deakin Business School, part of Deakin University's Faculty of Business and Law. He specializes in quantitative marketing, with expertise in marketing technology, customer lifetime value modeling, and market response analysis. Bonfrer holds a PhD from the University of Chicago Booth School of Business and has taught at institutions including the University of Melbourne and Singapore Management University. His research focuses on fast-moving consumer goods, digital marketing solutions, and adaptive market response models. Education: PhD in Business, University of Chicago (200?) MBA, University of Chicago (200?) Master of Commerce, University of Auckland Bachelor of Commerce, University of Auckland Research Interests: Marketing Technology and Analytics Customer Lifetime Value Modelling Digital Marketing Strategies Retail Competition Dynamics Brand Equity and Consumer Behavior His work bridges theoretical and applied research, addressing challenges in consumer decision-making, brand management, and policy impact (e.g., cigarette plain packaging regulations). Grants & Awards: $20,000 Conjointly AI Alignment Research Grant (2023) $18,181 Legal Super Pty Ltd Churn Management Contract (2022) ANZMAC Distinguished Researcher (2012), Best Paper Awards (2015) Multiple teaching and research fellowships (e.g., Lee Kuan Yew, Lee Foundation) Professional Contributions: Chair, ABDC Marketing Learning Standards Working Party (2011–2012) Member of AMA, ANZMAC, EMA, and INFORMS Supervision of 4 active PhD students and 1 completed student His research has been published in top journals like Marketing Science , Journal of Marketing Research , and Management Science , emphasizing data-driven insights for business decision-making.

Steve CassidyView profile

Associate Professor

• Research Data Management
• Linguistic Annotation Standards
• Speech Analysis Technology
• +3 more

Steve Cassidy is an Associate Professor in the School of Computing at Macquarie University. His research focuses on research data discovery, reproducibility, and accessible tools for language sciences. He specializes in linguistic annotation standards, speech analysis technologies, and interoperability infrastructure for human communication science. He holds a PhD in Computer Science from Victoria University of Wellington, an MSc in Knowledge-Based Systems from the University of Edinburgh, and a BSc in Physics with Astrophysics from the University of Leicester. He leads major projects such as FAIMS3 (Field Acquired Information Management Systems) and the Alveo Virtual Laboratory, which support digital data capture and management in diverse research contexts. His work emphasizes FAIR data principles and interdisciplinary collaboration, with applications in linguistics, archaeology, and speech technology. He has contributed to initiatives like the AusTalk corpus and Signbank, advancing open science practices and accessibility in research. Recent research highlights include machine learning models for military performance prediction and temporally rich deep learning approaches for magnetoencephalography analysis. His work has been recognized through awards including the 2024 Teaching Excellence Highly Commendation and the 2023 Inter-School Collaboration Award. Education: PhD in Computer Science (Victoria University of Wellington, 1993) MSc in Knowledge-Based Systems (University of Edinburgh, 1984) BSc in Physics with Astrophysics (University of Leicester, 1983) Postgraduate Certificate in Higher Education (Macquarie University, 2008) Key Projects: FAIMS3: Mobile field data collection platform Alveo Virtual Lab: Linguistic data repository AusTalk Corpus: Large-scale Australian English speech database His publications span machine learning applications, speech technology, and open research infrastructure. He actively collaborates across disciplines to develop tools that enhance reproducibility and accessibility in scientific research.

Muhammad IkramView profile

Senior Lecturer

• Cybersecurity
• Privacy-Preserving Systems
• Machine Learning for Security
• +5 more

Dr. Muhammad Ikram is a Senior Lecturer in Cybersecurity at Macquarie University's School of Computing, affiliated with the Information Security and Privacy (ISP) group and the Macquarie Cybersecurity Hub (MCHUB). His research focuses on privacy/security in mobile/Web platforms, leveraging machine learning for fraud detection and vulnerability analysis. He holds a PhD in Electrical Engineering from UNSW, a Master's from Ajou University, and a Bachelor's from UET Peshawar. Ikram has published 64+ works in top conferences/journals like Usenix Security, NDSS, and ACM TOPS. His notable contributions include analyzing iOS app vulnerabilities, tracking prevention mechanisms, and mobile health app privacy risks. He received the Best Paper Award at AsiaCCS 2019 and Outstanding Paper at Mobiquitous 2021. Research Interests: Mobile/Web Security, Malware Detection, Privacy-Preserving Systems Grants: Led projects on network security, telecommunications obligations, and cybersecurity hubs Media Impact: Featured in The Guardian and over 50 outlets for work on web resource loading (~11M audience reach) Service Roles: Technical Program Committee member at WWW/PETS, reviewer for CCS/PETS He previously held roles as a Postdoc at University of Michigan and Research Scientist at Data61. His work bridges academic research with real-world impact in cybersecurity policy and industry standards.

Yan LiView profile

Lecturer

• IoT
• Mobile Computing
• Wireless Sensor Networks
• +7 more

Dr. Yan Li is a Lecturer in IoT and Networking at the School of Computing, Macquarie University. She holds a PhD in Electrical and Electronic Engineering from the University of Melbourne and a Master of Research in Electronic Engineering from the University of Technology Sydney. Previously, she was a Research Fellow at the Telstra-UoW AIoT Hub. Her research focuses on IoT, inertial navigation, mobile computing, wireless sensor networks, and machine learning applications in sensor data analytics. She has been recognized with the Michael Richey Medal and a DIN Pilot Project Grant, and contributed to initiatives like the Future Communications Research Centre and Smart Green Cities Research Centre. Education: Bachelor of Engineering in Aerospace Engineering, Northwestern Polytechnical University PhD in Electrical and Electronic Engineering, University of Melbourne Master by Research in Electronic Engineering, University of Technology Sydney Research interests include: IoT and networking infrastructure Indoor localization and tracking systems Wearable sensor technologies Machine learning for sensor data analytics Healthcare mobility monitoring Smart home situational awareness Notable awards and grants include the Michael Richey Medal (2023), DIN Pilot Project Grant (2022-2023), and the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her work spans over 300 citations and 25 research outputs, emphasizing practical applications in telecommunications and smart environments. Advising and grants: Supervises PhD/Master students in IoT, localization, and machine learning. Active in collaborative projects like the Future Communications Research Centre, focusing on IoT and smart infrastructure solutions. Labs/Teams: Affiliated with Macquarie's Future Communications Research Centre and Smart Green Cities Research Centre, advancing IoT and sustainable urban technologies.

Rajan ShankaranView profile

Senior Lecturer

• Network Security
• Mobile Communication Protocols
• Internet of Things
• +4 more

Rajan Shankaran is a Senior Lecturer at Macquarie University's School of Computing, part of the Faculty of Science and Engineering. He serves as Course Director for postgraduate programs in Networking and IoT, and chairs the IEEE VTS NSW Chapter. His roles include membership in the University Academic Senate Curriculum Subcommittee and representation on the FSE Faculty Board. Education: MBA (MIS) from Maastricht School of Management; M.Sc. (Hons.) and Ph.D. in Network Communications and Security from University of Western Sydney. Research focuses on Network Security, Mobile Communication Protocols, IoT, Network-on-Chip, and Medical Implant Security. He has led projects like 'Digital Health Records Storage and Analysis for Global Patients' and organized IEEE events such as NOMS 2017. Notable awards include IEEE Senior Member status. Recent work includes publications on wireless network security, optimization algorithms, and IoT-enabled healthcare systems. He advises on over 70 research outputs and has contributed to 4 major projects addressing cybersecurity and network efficiency. Labs/Teams: Active in the Future Communications Research Centre, collaborating on 5G/IoT security and edge computing applications.

Michael ShengView profile

Professor

• Web of Things
• Internet of Things (IoT)
• Big Data Analytics
• +5 more

Dr. Michael Sheng is a Distinguished Professor and Head of the School of Computing at Macquarie University. He previously served at the University of Adelaide's School of Computer Science for a decade in senior roles such as Interim Head of School and Deputy Head. His academic journey includes a PhD from the University of New South Wales (UNSW), a postdoc at CSIRO ICT Centre, and a visiting research fellowship at UNSW. With industry experience as a senior software engineer for six years, he bridges academia and practice. His research focuses on Web of Things, IoT, Big Data Analytics, and Service-oriented Computing. Recognized for his impact, he holds awards like the AMiner Most Influential Scholar Award on IoT (2019) and ARC Future Fellowship (2014). He leads initiatives in Smart Green Cities and is active in IEEE and ACS advisory roles. Recent projects include Revolutionizing Dynamic Pricing and Management Optimization through Synergizing Generative AI, Predictive Analytics, and Game Theory (2024–2028) and Central Bank Digital Currency – Infrastructure & Applications (2023–2028). He supervises students in cutting-edge areas like fraud detection and federated learning, as evidenced by awards like the 2021 IEEE ICDM Best Paper Award. Sheng's affiliations span multiple research centers at Macquarie, including the Frontier AI Research Centre and Smart Green Cities Research Centre, reflecting his interdisciplinary contributions.

Dinusha VatsalanView profile

Senior Lecturer

• Privacy-Preserving Technologies
• Record Linkage
• Data Mining
• +4 more

Dr. Dinusha Vatsalan is a Senior Lecturer in Cyber Security at the School of Computing, Macquarie University. She joined in May 2022 and holds a PhD in Computer Science from Australian National University and a BSc (Hons) in Information and Communication Technology from the University of Colombo School of Computing, Sri Lanka. Previously, she was a Research Scientist at Data61, CSIRO. Her research focuses on privacy-preserving technologies, including record linkage, data mining, machine learning, and privacy risk quantification. She has authored over 60 scientific articles and received notable awards such as the Australian government’s Endeavour Postgraduate Research Award, Ruby Payne-Scott Award (CSIRO), and the Manthan Award South Asia (2009). Her work spans privacy-preserving data sharing, adversarial machine learning defenses, and secure data integration across domains like healthcare and mining. Recent publications highlight contributions to trusted data sharing in mineral exploration, privacy-preserving deep learning models, and adversarial training defenses. She is affiliated with the Future Communications Research Centre and actively collaborates internationally on cyber security and privacy initiatives.

• Intelligent Mechatronics
• Robotics
• Agricultural Automation
• +5 more

Associate Professor Tien-Fu Lu is a faculty member in the School of Electrical and Mechanical Engineering at the University of Adelaide, affiliated with the Faculty of Sciences, Engineering and Technology. He holds a Reader position and specializes in intelligent mechatronics, robotics, and automation systems. His work spans nano-positioning technologies, industrial robotics, agricultural automation, and mining engineering. Research Interests: - Robotics and mechatronics in agriculture (vineyard pruning, stockpile management) - Underwater robotics and autonomous systems - Energy harvesting using piezoelectric materials - Human-machine interaction through exoskeletons - Precision motion control and sensor systems Recent work focuses on automated pest detection in agriculture, real-time stockpile modeling for mining, and advanced robotic systems for industrial applications. He has secured over $12M in research funding, including major grants from CSIRO, GRDC, and SmartSat CRC. Awards: Web of Science High-Citation Paper (2018) ASPEN2015 Conference Best Paper Award StateWeb Excellence Award (2007) Grants and Supervision: He leads teams in multidisciplinary projects, supervising Masters/PhD candidates in robotics and mechatronics. Active collaborations involve industry partners like CSIRO, GRDC, and Wine Australia. Labs/Teams: - Robotics and Automation Lab - Agricultural Mechatronics Group - Mining Automation Research Cluster

• Labour Economics
• Health Economics
• Education Economics
• +5 more

Dr. Zhang Wei is a Grant-Funded Researcher (C) at the University of Adelaide's School of Economics and Public Policy, affiliated with the Future of Employment and Skills Research Centre. He holds a PhD in economics and has held roles including Associate Lecturer at the University of Adelaide and Senior Research Fellow at Flinders University's National Institute of Labour Studies. His research focuses on labour economics, health economics, education economics, international trade, and applied game theory. Current projects include evaluations of the National Disability Insurance Scheme, impacts of ageing populations on productivity, job mismatches, and education's role in labour markets. Education: PhD in economics (thesis on multilateral vs. bilateral trade liberalisation) Research interests span policy-relevant areas such as workforce dynamics, healthcare economics, and trade agreements. He has contributed to projects funded by ARC/NHMRC, NCVER, and government departments, addressing topics like retirement policy, VET completers' job mobility, and economic security. Supervision eligibility includes co-supervision for Masters/PhD candidates.

Xuyun ZhangView profile

Associate Professor

• Scalable Machine Learning
• Big Data Privacy
• Cyber Security
• +5 more

Dr. Xuyun Zhang is an Associate Professor at the School of Computing, Macquarie University, Australia. He holds a PhD from University of Technology Sydney (2014), and Master's/Bachelor's degrees from Nanjing University (China). His research focuses on scalable machine learning, big data privacy, cyber security, edge computing, and IoT. He has led 11 research projects including Next-Gen Banking (2024-2028) and Scalable Document Intelligence (2022-2027). His work spans secure AI, privacy-preserving techniques, and edge-cloud collaboration. He has received awards such as the Chancellor's Best PhD Thesis (2015) and Early-Career Research Excellence (2019). His recent publications address areas like metaverse service offloading, hypergraph trust prediction, and federated learning frameworks. He actively organizes conferences like the Australasian Information Security Conference (2017) and chairs industry-research initiatives in cloud computing and mobile networking. Education: PhD (UTS, 2014), MEng (Nanjing University, 2011), BSc (Nanjing University, 2008). Teaching includes Machine Learning in Cybersecurity, Data Science, and Software Engineering. Key research contributions include privacy-preserving publishing, edge computing offloading methods, and blockchain-based secure access management. His projects often integrate AI with cloud-edge systems to address real-world challenges in finance, healthcare, and smart manufacturing.

Quan Z. ShengView profile

Professor

• Internet of Things
• Big Data Analytics
• Machine Learning
• +3 more

Quan Z. Sheng is a Distinguished Professor and Head of the School of Computing at Macquarie University, Australia. Previously, he held senior leadership roles at the University of Adelaide. He earned his PhD from the University of New South Wales (UNSW) and has extensive industry experience as a senior software engineer. His research focuses on Internet of Things (IoT), Big Data Analytics, Machine Learning, and Web Science, with significant contributions to services computing and trust management. Sheng has received notable awards, including the AMiner Most Influential Scholar Award (2018) and ARC Future Fellowship (2014). His research interests span IoT service ecosystems, graph-based machine learning, and federated learning. Recent achievements include advancements in graph-level learning and fraud detection systems. Sheng leads major grants like the ARC Research Hub in IoT for Water and collaborates with industry through CRC partnerships. His team has produced over 500 publications, with impactful work featured in Communications of the ACM and top conferences like IEEE ICDM. Sheng advises a diverse group of PhD students and oversees research centers including the Data Horizons and Smart Green Cities. Awards: AMiner Most Influential Scholar Award on IoT (2018) ARC Future Fellowship (2014) Chris Wallace Award for Outstanding Research (2012) Best Paper Awards at IEEE ICDM 2021 and PDCAT 2019 Grants & Collaborations: Leads ARC Industrial Transformation Hubs ($10M+), Digital Finance CRC, and Smart Cities projects. Active in IEEE Technical Communities and ACS IoT Advisory Board. Teams & Labs: Directs research groups in AI, IoT, and data science. Collaborates globally through visiting scholars and industry partnerships.