Dr. Jusak Jusak is a Senior Lecturer in Internet of Things (IoT) at the School of Science and Technology, James Cook University Singapore. He holds a PhD in Electrical Engineering from RMIT University and a BEng in Electrical Engineering from Brawijaya University, Indonesia. His research focuses on IoT applications in telehealthcare, blockchain for healthcare IoT, and lightweight machine learning for constrained devices. Dr. Jusak has held academic positions at Dinamika University (Surabaya, Indonesia) and Massey University (New Zealand). He has secured grants including a Cross-Campus Collaboration Grant for blockchain development and a Teaching Innovation Grant for the VetheRBlocks project. He is a Guest Editor for the Electronics journal's special issue on wireless communication security. His research interests include IoT device development for medical signal analysis, secure cloud-based healthcare systems, and hybrid-channel communication for rural healthcare. He has published over 20 peer-reviewed articles and received awards such as the ICITAS 2018 Best Paper Award and a 2022 Futuristic Technology Award for IoT-based water quality monitoring. Dr. Jusak's work emphasizes practical IoT solutions for healthcare challenges, integrating machine learning and blockchain to enhance data security and accessibility in distributed networks.
Professor Tanveer Zia at Charles Sturt University is a leading researcher in cybersecurity, focusing on wireless sensor networks, IoT security, and privacy-preserving data sharing. He leads the Privacy-Preserving Data Sharing in Hyperconnected World research theme and serves as CSU lead in the Cyber Security Cooperative Research Centre (CRC). PhD in Information Technology from the University of Sydney Active in cybersecurity research for over 15 years Extensive publications on wireless sensor network security, biometric security, and cloud security His work spans human-centric cybersecurity , machine self-control , and biomedical signal processing . Recent projects include developing frameworks for local differential privacy and security awareness programs for regional NSW youth. Key article themes include: advanced privacy mechanisms, IoT vulnerabilities, and deep learning applications in biometric systems. Notably, he explores user-driven privacy and cyber diversity metrics for resilient systems. Scientific Awards Emerging Researcher Award (2010) Research Excellence Awards (2010, 2011) Leadership Excellence Award (2013) Academic Excellence Award (2014) Prof Zia supervises doctoral students in digital forensics and contributes to cybersecurity education. His media engagements include appearances on identity theft, computer scams, and youth cybersecurity initiatives.
Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.
Associate Professor Jacqui Romero is an expert in experimental quantum information science at the University of Queensland (UQ), leading the Qudits@UQ research group. She holds a PhD from the University of Glasgow and has held prestigious fellowships including the ARC DECRA (2016) and Westpac Research Fellowship (2019). Her research focuses on high-dimensional quantum systems (qudits), exploring their applications in quantum technologies such as communication, computation, and error correction. She is affiliated with the ARC Centre of Excellence for Engineered Quantum Systems (EQUS) and teaches quantum technologies at UQ. Education: Bachelor of Applied Physics (magna cum laude), University of the Philippines Diliman Master of Science in Physics, University of the Philippines Diliman Doctor of Philosophy, University of Glasgow Research Interests: Dr. Romero’s work emphasizes generating and characterizing high-dimensional quantum states, designing quantum devices via inverse optimization, and testing indefinite causal order phenomena. Her team explores quantum error correction, quantum imaging, and the advantages of high-dimensional entanglement in robust quantum communication. Awards: L’Oréal-UNESCO For Women In Science Award (2017) Ruby Payne-Scott Medal (2018) L’Oréal-UNESCO International Rising Talent Award (2019) Grants & Funding: ARC Training Centre in Current and Emergent Quantum Technologies UQ Major Equipment Grant for a multimode optical waveguide facility Labs & Teams: Qudits@UQ, a dedicated experimental group advancing quantum technologies through photonics-based high-dimensional systems research.
Dr. Abigail Koay is an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. Her research focuses on cybersecurity, machine learning applications in industrial systems, and healthcare technology. She has contributed to projects such as real-time cyber-attack detection using weakly supervised learning, supported by UQ Cyber Seed Funding (2021–2022). Her work spans multiple disciplines including: Cybersecurity for Industrial Control Systems (ICS) IoT network anomaly detection using fog-assisted frameworks Machine learning for medical imaging (e.g., glaucoma detection) AI-driven cybersecurity strategies for smart grids Recent publications highlight advancements in: Irregular time series analysis using GNNs Positive-unlabeled learning with random forests Domain generalization in retinal image analysis Dr. Koay has authored/co-authored 15+ peer-reviewed articles across journals like Frontiers of Computer Science , IEEE Access , and conferences including NeurIPS and ISGT Asia. She collaborates with industry partners on projects like Plan2Defend for smart grid security and SDGen for synthetic cybersecurity dataset generation. Her research integrates theoretical machine learning with practical cybersecurity challenges, emphasizing real-world applicability in critical infrastructure and healthcare systems.
Prof James Smithies is a Professor and Director of the HASS Digital Research Hub at the ANU College of Arts and Social Sciences. He specializes in Digital Humanities and Research Software Engineering, applying computational methods to arts and social sciences. His work includes developing digital archives, analyzing cultural data, and managing post-disaster information systems. He leads projects like the AI as Infrastructure initiative and the Australian Cultural Data Engine. Previously, he held roles at King’s College London and the University of Canterbury. Education: Doctorate in History of Ideas from University of Canterbury (Aotearoa/New Zealand). Research interests focus on interdisciplinary digital humanities, cultural heritage preservation, and the ethical use of large language models. He has supervised research students and contributed to over 50 publications. Current projects include the Social Science Research Infrastructure Network (2024-2028) and ANU Futures Scheme funding (2024-2026).
Associate Professor Binghao Li leads the MIoT & IPIN Lab at the School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney. He holds a PhD in Spatial Information Systems from UNSW and advanced degrees in Civil and Electrical/Mechanical Engineering from Tsinghua University and Beijing Jiaotong University. Expertise in indoor/outdoor positioning systems Pioneering mine IoT applications Leader in pedestrian navigation research His research spans indoor positioning technologies, satellite navigation, and mining IoT solutions. Key grant projects include: 2021 CRC-P grant ($2m) for underground mine LoRa networks 2020 ARC Research Hub ($5m) for connected sensors 2018 Digital Grid Seed Funding for indoor navigation 2015-2019 ARC Linkage grants for positioning systems Award highlights: 2019 Best Paper & Presentation Awards 2010 VC's Post-Doctoral Fellowship 2004-2005 student research awards He supervises research in indoor positioning and mine IoT, and teaches courses including ENGG1000 Engineering Design and MINE8710 Mine Slope Stability.
Professor Wen Hu is a distinguished academic at the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he holds a professorship focusing on cutting-edge research in cyber-physical systems and the Internet of Things (IoT). With extensive experience in both academia and industry, Professor Hu has established himself as a leading researcher in sensor network systems, low-power communications, security, and compressive sensing. His work bridges theoretical research with practical applications, as evidenced by his active commercialization efforts through roles as Chief Technology Officer at Parking Spotz and former Chief Scientist at WBS Tech. Professor Hu's research interests span multiple domains within cyber-physical systems and IoT. His work particularly focuses on low-power wireless communications , sensor network security , compressive sensing techniques , and novel applications of IoT technologies . His research group explores how these technologies can be applied to solve real-world problems in areas such as smart buildings, environmental monitoring, and human-computer interaction. The research integrates hardware design, communication protocols, security mechanisms, and data analytics to create efficient and robust IoT systems. Professor Hu's publication record demonstrates a consistent focus on advancing the state-of-the-art in wireless sensor networks and IoT. His recent work shows increasing integration of machine learning techniques with traditional sensor systems, exploring applications in areas ranging from healthcare monitoring to agricultural technology. There's a clear trend toward more sophisticated signal processing approaches, particularly using mmWave technology and visible light communications, while maintaining the core focus on energy efficiency and security that has characterized his earlier work. CSIRO Office of Chief Executive (OCE) Julius Career Award (2012-2015) Professor Hu has successfully secured multiple research grants from the Australian Research Council, CSIRO, and industry partners, enabling his team to pursue ambitious research projects with both theoretical significance and practical applications. His editorial leadership as Editor-in-Chief of ACM TOSN and organizational roles in major conferences like CPS-IoT Week 2020 and ACM/IEEE IPSN 2023 demonstrate his significant contributions to the research community. As a senior member of both ACM and IEEE, he actively mentors students and early-career researchers while fostering collaborations across academia and industry. Professor Hu leads a vibrant research group at UNSW that combines theoretical research with practical implementation. His team works closely with industry partners to ensure research relevance and facilitate technology transfer. The group maintains strong connections with international research communities and participates in collaborative projects that address global challenges in IoT and cyber-physical systems.
Frank den Hartog is a Research Chair in Critical Infrastructure and Information Systems at the University of Canberra . He is also an Adjunct Fellow at UNSW Canberra (Australian Defence Force Academy) and has held academic roles including Associate Professor at the University of New South Wales. His career spans industry and academia, with expertise in cybersecurity, IoT, and wireless networking. Education PhD in Physics and Mathematics from Leiden University (1998) MSc in Applied Physics from Eindhoven University of Technology (1992) Research Interests: Dr. den Hartog focuses on Zero Trust Architectures , Physical Layer Security , and Secure Industry 4.0 , with a specialization in protecting Critical Infrastructure through advanced cybersecurity frameworks. His work bridges theoretical optimization with practical implementations in Cyber-Physical Systems and Programmable Networks . Academic Contributions: He co-authored 82 peer-reviewed articles and contributed to 67 standards, including 7 as co-editor. His publications reveal trends in Smart Home Security , AI-Defined Networking , and Trust Management in IoT , reflecting his commitment to securing interconnected systems. Teaching & Leadership: Dr. den Hartog supervised 7 Masters/Honours and 2 PhD students, served on 8 PhD exam committees, and was Chair of the Home Gateway Initiative's Technical Working Group (2012-2016). He actively participates in conference organizing committees and journal reviewing.
Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)
Professor J. Joshua Thomas is a distinguished academic at University of Wollongong Malaysia, specializing in intelligent systems and advanced computational techniques. He holds a PhD in Intelligent Systems Techniques from Universiti Sains Malaysia (2015) and a Master's degree in Computer Science from Madurai Kamaraj University, India (1999). Professor Thomas has demonstrated strong leadership in academic administration, having served as Head and Deputy Head of the Department of Computing between 2012 and 2017. His research focuses on intelligent systems and interdisciplinary computational algorithms, with recent work emphasizing Deep Learning, Graph Convolutional Neural Networks (GCNN), Graph Recurrent Neural Networks (GRNN), Hyper-Graph Attention Networks, and Quantum Machine Learning. Professor Thomas's projects span diverse applications including end-to-end steering learning systems, algorithm design in drug discovery, and advanced data analytics for electricity consumption and carbon price forecasting. Professor Thomas maintains an impressive publication record with over 50 peer-reviewed publications and 12 books with publishers including Wiley, Elsevier, and IGI Global. His research is supported by multiple active grants at institutional, national, and international levels, including projects on cancer survivor well-being and AI bias in smart cities. Best Paper Award at SCI 2025 Oracle Cloud Infrastructure 2024 Generative AI Certified Professional 2022 Global Staff Awards (UOWGE) - Excellence In Research Winner V-MIIEX2021: COVIDNet - GOLD AWARD Fundamental Research Grant Scheme (FRGS) recipient (2019) As an active researcher and educator, Professor Thomas serves as Principal Investigator on multiple grants, supervises PhD and Master's students, and regularly delivers keynote addresses at international conferences. His collaborative spirit is evident through partnerships with institutions in India, USA, China, and Germany, fostering knowledge exchange and innovation across disciplines.
Dr. Keshav Sood is a Senior Lecturer at Deakin University's School of Information Technology, where he leads research in cybersecurity, AI, and next-generation networks. His affiliations include roles as Graduate Research Coordinator and South Asia Country Coordinator. He completed his PhD at Deakin University and a post-doctoral fellowship at the University of Newcastle. Research Interests: Dr. Sood focuses on securing distributed systems, with emphasis on: Federated learning for intrusion detection in IoT/5G networks Biometric privacy in immersive technologies (VR/AR) RF fingerprinting for IoT device authentication Quantum-resistant software-defined networks Adversarial robustness in voice authentication systems His publications consistently explore AI-driven security frameworks, with recent work addressing data sparsity in IoT sensors, cross-domain IIoT authentication, and phishing mitigation using large language models. Awards: Professor of IT Award (2016) IEEE TNSE Excellent Reviewer (2023) Deakin HDR Supervision Award (2023) Course Team Award for Industry Certification Alignment (2021) Supervision & Grants: Dr. Sood currently advises 8 graduate researchers and has secured $655,313 in competitive funding. Key projects include: Smart Farming Cyber Resilience (DFAT Maitri Grant) Secure Access for Critical Infrastructure (Cyber CRC) IoT Data Integrity for Defense Systems (Australian Defence) He leads the Deakin Cyber Research and Innovation Centre, focusing on scalable security solutions for industry partners.
Dr. Matt Felicetti is a dedicated Lecturer in Engineering at La Trobe University's Bendigo campus, specializing in robotics, electronics, and artificial intelligence. He holds a PhD in randomized artificial intelligence for industrial applications and a bachelor's degree in computer systems engineering with top honors. Matt plays a pivotal role in enhancing the engineering capstone program and teaches subjects including Robotic System Design, Advanced Research, and Advanced Engineering Innovation. His educational background includes: PhD in Randomized Artificial Intelligence for Industrial Applications, La Trobe University (2019-2022) Bachelor of Engineering (Computer Systems) with First Class Honors, La Trobe University (2013-2016) Advanced Diploma of Electronics, Swinburne University of Technology (2011-2012) Matt's research primarily focuses on collaborative industry partnerships through the RAMPS R&D group, implementing innovative engineering solutions using electronics, sensors, embedded systems, robotics, AI, and algorithm design. He has a particular interest in field robotics and machine vision in agriculture. Beyond industry applications, Matt specializes in randomized artificial intelligence algorithms, particularly Stochastic Configuration Networks, with a focus on optimizing these algorithms for industrial environments. His research extends to low-level computing aspects including binary operations, data encoding, and hardware implementation on FPGAs or small embedded devices. His recent publications demonstrate a strong progression from theoretical algorithm development to practical implementations in specific industry contexts, with increasing attention to hardware implementation and agricultural robotics applications. The research shows a clear trajectory toward real-world industrial problem solving. Matt has received numerous scientific awards and recognitions: David Myers Medal Nancy Millis Medal SEMS Teaching Award for Industry Relevance and Student Engagement David Myers Research Scholarship D.M. Myers University Medal Fellow of the Higher Education Academy (FHEA) In terms of academic service, Matt serves as an Associate Editor for the journal Industrial Artificial Intelligence and has conducted peer reviews for Neural Computing and Applications, IEEE Transactions on Industrial Informatics, and Information Sciences. He currently leads the funded research project "Robotic Based Sewer Pipe Condition Assessment" through SmartCrete CRC (2024-2027). His teaching portfolio includes coordinating Robotic System Design, Advanced Research, and Ideas for Innovation courses. Matt is an active member of the RAMPS R&D group, which focuses on crafting innovative solutions for industry-specific problems. His work bridges academic research with practical industry applications, particularly in agricultural robotics and industrial AI implementation, demonstrating strong collaboration with researchers like Ross R, Wang D, and Putland S.
