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Abigail KoayView profile

Research Fellow

• Cyber Security
• Machine Learning
• Industrial Control System (ICS) Security
• +3 more

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.

Elizabeth WilliamsView profile

Senior Lecturer

• Nuclear Physics
• Artificial Intelligence Ethics
• Policy Analysis
• +3 more

Dr Elizabeth Williams is a Senior Lecturer at the ANU Centre for European Studies and an Honorary Senior Lecturer in the ANU Department of Nuclear Physics. She holds a B.S. (Intensive) in Physics from Yale University (2004) and a Ph.D. in Experimental Nuclear Structure from Yale (2009). Her research bridges nuclear physics and applied ethics, focusing on AI governance and responsible innovation. As a Research Fellow at the 3A Institute, she develops frameworks for managing data, AI, and cyber-physical systems in societal contexts. Her work emphasizes anticipatory governance, ethical AI implementation, and the societal impacts of emerging technologies. Her academic journey includes postdoctoral roles at Yale and CSIRO, an ARC DECRA fellowship at ANU (2012–present), and leadership in transdisciplinary research programs. Key interests include fusion-fission dynamics in superheavy elements, trustworthy autonomous systems, and policy around AI ethics. She has contributed to projects like the AI Observatory for the Nuclear Sector and studies on rehabilitation robotics in healthcare. Williams has published extensively on nuclear physics phenomena (e.g., quasifission, fission dynamics) and AI-related topics such as voice data documentation, trust in human-machine systems, and defense technology implications. Her research highlights the intersection of science and policy, advocating for proactive approaches to technological development's societal consequences.

• Systems and Signal Processing
• Control Theory
• Ill-Conditioned Inverse Problems
• +7 more

Professor Victor Solo serves as Director of Research with the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW). With an extensive academic career spanning over four decades since earning his PhD from the Australian National University in 1979, he has established himself as a leading expert in multiple interdisciplinary fields. University: University of New South Wales School: School of Electrical Engineering and Telecommunications Department: Electrical Engineering and Telecommunications Position: Professor and Director of Research Professor Solo received his BSc from the University of Queensland, followed by a BSc (first class honors) and BE (first class honors) from UNSW, culminating in a PhD from ANU in 1979. His educational background provided the foundation for his diverse research career spanning engineering, mathematics, and biomedical applications. His research interests encompass a wide range of theoretical and applied topics, with particular emphasis on Systems and Signal Processing, Control Theory, and Ill-Conditioned Inverse Problems. He has made significant contributions to Econometrics and Time Series Analysis, developing innovative approaches to System Identification. His work extends into biomedical domains through research in Medical Imaging and Computer Vision, as well as Neuroengineering through studies of Neural Coding and Point Processes. Professor Solo's interdisciplinary approach bridges theoretical mathematics with practical applications across engineering and medical fields. Analysis of Professor Solo's recent publications reveals a strong focus on advanced statistical modeling techniques, particularly in time series analysis and point process modeling. His work consistently addresses stability and identifiability challenges in complex models, with recent publications exploring Vector Autoregressive models, Hawkes processes, and stochastic differential equations on manifolds. The research demonstrates a progression from foundational theoretical work to increasingly sophisticated applications in network modeling and biomedical signal processing. Professor Solo has maintained an exceptionally productive research career with publications spanning from 1981 to the present, demonstrating remarkable longevity and adaptability in his research focus. His work shows consistent contributions across multiple high-impact journals including IEEE Transactions on Signal Processing, Automatica, and Neural Computation. While specific awards are not listed in the available information, his sustained publication record in top-tier journals indicates significant recognition within his fields of expertise. As Director of Research, Professor Solo likely oversees research strategy and development within the School of Electrical Engineering and Telecommunications. His extensive publication record suggests active supervision of graduate students and postdoctoral researchers, though specific names of advisees are not provided in the available information. His research has likely attracted substantial grant funding given the scope and duration of his work across multiple domains.

• Acoustics and noise control
• Dynamics, vibration and vibration control
• Signal processing
• +3 more

Dr. Sipei Zhao is a Senior Lecturer at the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), where he has been employed since January 2021. Prior to this, he worked as a Post-doctoral Research Fellow at the Centre for Audio, Acoustics and Vibration of UTS from July 2018 to December 2020. His educational background includes a PhD in Electrical Engineering from RMIT University (2018), a Master's degree in Acoustics from Nanjing University (2015), and a Bachelor's degree in Electronics Engineering from Nanjing University (2012). Dr. Zhao's research focuses on acoustics and noise control, dynamics and vibration control, and signal processing. His work spans theoretical development and practical applications in active noise control, architectural acoustics, and machine learning for acoustic analysis. He has made significant contributions to distributed acoustic systems, neural network applications in sound field reconstruction, and innovative approaches to sound zone control. His recent publications demonstrate a strong trajectory in distributed acoustic systems, neural network applications in acoustics, and innovative approaches to sound field control and reproduction. His work combines theoretical rigor with practical implementations, often resulting in significant reductions in computational complexity while maintaining or improving performance. Best Student Paper at the 22nd International Congress on Sound and Vibration (ICSV22) in 2015 Best Paper Prize at the 21st International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRA2016) in 2016 Best Paper Award at the 23rd International Congress on Acoustics (ICA2019) in 2019 Young Professionals Grant from the International Institute of Noise Control Engineering (I-INCE) Ford Publication Commendation Prize from RMIT University in 2017 ARC Early Career Industry Fellowship in 2024 Dr. Zhao has successfully secured multiple research grants and contracts from organizations including the Australian Research Council, NSW Environment Protection Authority, and industry partners like Noizend Pty Ltd. His research has practical applications in industrial settings, transportation, and building acoustics. He serves as a peer reviewer for prestigious journals including the Journal of Sound and Vibration and IEEE/ACM Transactions on Audio, Speech, and Language Processing.

• Sport Analytics
• Wearable Sensors
• Athletic Performance
• +7 more

Dr. Alice Sweeting is a Research Fellow at Victoria University's College of Sport, Health & Engineering, with a co-appointment at the Western Bulldogs Football Club. Her work bridges sport science, wearable technology, and data analytics to enhance athletic performance in team sports such as Australian Football and netball. PhD, Victoria University, 2017 BAppSc (Hons), Victoria University, 2012 Her research focuses on using wearable sensors, inertial measurement units, and machine learning to analyze athlete movement, skilled behavior, and performance under various constraints. She applies ecological dynamics and the constraints-led approach to understand how individual, task, and environmental factors shape performance in real-world contexts. Her recent publications reveal a strong trend in the application of advanced statistical methods (e.g., change point analysis), machine learning for movement classification, and the development of representative training designs. She frequently publishes in high-impact journals such as Journal of Sports Sciences and Frontiers in Physiology , often collaborating with experts in biomechanics, sports analytics, and motor learning. Alice is actively involved in research supervision, currently co-supervising six postgraduate students on projects related to kicking performance, player tracking, and tactical behavior in AFL. She has secured competitive research funding, including a Fusion Sport Innovation Connections grant focused on health and fitness monitoring in racquet sports. Member, Course Advisory Committee (CAG), 2025 She is also engaged in professional outreach through her blog on programming in R, Twitter, and LinkedIn, demonstrating a commitment to knowledge translation and open science.

Jyoti Joshi DhallView profile

Senior Lecturer

• Affective Computing
• Multimodal Behaviour Analytics
• Digital Health
• +4 more

Jyoti Joshi Dhall is a Senior Lecturer in the Department of Human Centred Computing at Monash University, where she conducts interdisciplinary research at the intersection of artificial intelligence, behavioral analytics, and digital health. Her work contributes to UN Sustainable Development Goals, particularly in health and well-being. Her research focuses on affective computing , multimodal behavior analytics , and human-centered AI , with applications in mental and physical health monitoring. She employs advanced machine learning techniques to analyze behavioral signals such as facial expressions, speech, and body movements for detecting conditions like pain, apathy, and depression. Recent publications show a strong trend in applying deep learning models—such as LSTM-DNN and anomaly detection networks—to clinical and assistive technologies. Her work appears in top-tier venues including IEEE conferences and the ACM Handbook series, reflecting impactful contributions to multimodal user interfaces and emotion-aware systems. She has collaborated with international researchers and her work has been cited in Scopus and referenced in patents. Although no specific awards are listed, her research has attracted attention from news outlets and academic platforms like Mendeley. Jyoti Joshi Dhall advises students and likely supervises research projects in human-centered computing, though specific advisees are not listed. Her research is supported by institutional and possibly external grants, given the scope and publication record. She is part of a broader research network focused on AI for healthcare, contributing to both theoretical and applied advancements in the field. She is affiliated with a research team or lab working on multimodal interaction and affective computing, though the specific lab name is not mentioned in the provided text.

Md Ashraful AlamView profile

Researcher

• Maternal health
• Child malnutrition
• Adverse childhood experiences
• +7 more

Md Ashraful Alam is a public health researcher at the University of Queensland's Poche Centre for Indigenous Health. He holds an RTP scholarship for his PhD research on environmental exposures in pregnancy and birth outcomes in Queensland . With prior affiliations at icddr,b in Dhaka, Bangladesh, his work bridges statistical analysis and epidemiological studies. M.Sc in Statistics, Rajshahi University MPH in Epidemiology, University of South Asia His research focuses on maternal and child malnutrition , adverse childhood experiences , mental health , and environmental enteric dysfunction . Recent studies examine gut biomarkers, nutritional interventions, and pandemic impacts on vulnerable populations. Key publications include machine learning models for pediatric mortality prediction , L-carnitine supplementation trials , and systematic reviews on indigenous health outcomes . Collaborations span institutions like Harvard University , University of Virginia , and icddr,b .

Wen HuView profile

Professor

• Cyber-physical systems
• Internet of Things
• Sensor network systems
• +3 more

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.

Sim Kwan HuaView profile

Lecturer

• Time Series Analysis
• Time Series Pattern Recognition
• Data Mining
• +2 more

Sim Kwan Hua serves as Head of Department for Computer Science and Software Engineering and Lecturer at Swinburne University of Technology Sarawak Campus's Faculty of Engineering, Computing and Science. Joining the institution in 2007 after 6 years in ICT academia, he became Computer Science Program Coordinator in 2011 and has established himself as a key researcher in data-intensive computational fields. His academic credentials include: Bachelor of Information Technology in Computer Science (Honours), Universiti Malaysia Sabah Master of Science in Information Technology, Universiti Malaysia Sarawak Dr. Sim's research program centers on advanced analytical methodologies: Time Series Analysis for dynamic system modeling Time Series Pattern Recognition in complex datasets Data Mining techniques for knowledge discovery Statistical frameworks for data interpretation Predictive Analytic systems for real-world applications His research impact is demonstrated through significant grant funding: Smart Waste Collection System with Intelligent Data Optimization (SDEC TRG 2021, MYR 121,800.00, 2022-2024) Scalable Pattern Recognition in Financial Time Series Data (FRGS 2020, MYR 58,390.00) Prospective graduate students are encouraged to contact Dr. Sim via email to explore research opportunities in his data analytics specialization areas, where he actively mentors the next generation of computational scientists.

• Suicide prevention
• Epidemiology
• Mental Health
• +3 more

Sithum Munasinghe serves as an Associate Research Fellow in Health Data Analytics at Western Sydney University, operating within a distinctive tripartite institutional framework spanning the School of Computer, Data, and Mathematical Sciences, the Translational Health Research Institute, and Western Sydney Primary Health Network. With over six years of specialized research experience, he leads data-driven initiatives to transform mental health service delivery through epidemiological insights. His academic foundation includes: PhD in Health Data Analytics from Western Sydney University BSc (Special) in Mathematics from University of Ruhuna, Sri Lanka Dr. Munasinghe's research program centers on suicide prevention through advanced epidemiological methods, with dual focus areas: Mental Health Systems Innovation : Developing predictive models for self-harm risk using linked administrative datasets, directly informing clinical service redesign Global Health Applications : Adapting intensive care risk prediction tools for resource-limited settings in low/middle-income countries His methodological expertise bridges statistical modeling and public health practice, leveraging large-scale health records to identify intervention points in mental healthcare pathways. Current collaborations integrate academic research with frontline primary care networks to implement evidence-based suicide prevention strategies across Western Sydney's diverse communities.