جست‌وجوی استاد و پژوهشگر

Yee Ling Booمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Image Processing
• Information Systems
• +۵ مورد دیگر

Dr. Yee Ling Boo is a Senior Lecturer in the Department of Accounting, Info Sys & Supply Chain at RMIT University in Australia. Her research focuses on Artificial Intelligence, Image Processing, Information Systems, Finance, and Business Analytics . She actively supervises Masters and PhD students in projects related to Large Language Models for Policy Analysis, Machine Learning in Social Services, and AI Readiness . Her teaching interests include Data Mining, Big Data, Business Analytics, and Cognitive Computing with applications across Business, Health, and Education . Recent publications highlight her work in Automated vehicle damage detection using AI Configurational approaches to cloud service provider selection Multimodal frameworks for car damage localization Decision-making agility through embedded analytics Community detection in multi-layer dark networks She has developed innovative methods for unsupervised expertise estimation in crowdsourcing , feature selection in imbalanced data , and Shari'ah-compliant stock analysis , with applications spanning fraud detection, spammer analysis, and educational technology . Her work bridges theoretical advancements with practical implementations for industry partners like Carsales and Baptcare .

• Probabilistic Methods
• Complex Networks
• Network Simulation
• +۲ مورد دیگر

Max Glonek is a Lecturer in Statistics at the School of Computer and Mathematical Sciences, University of Adelaide. His research focuses on developing probabilistic methods for analyzing complex networks, with applied work in political voting behavior and network polarization. Glonek's research interests center on probabilistic modeling, network simulation, and computational statistics. Key applications include measuring political partisanship through voting network analysis and developing algorithms for graph-based machine learning. His publications demonstrate consistent focus on network analysis techniques (2018-2019) and foundational work in directional time series modeling (2015-2016). Research integrates computer science, statistics, and domain-specific applications in political science and geophysics.

Abigail Koayمشاهده پروفایل

پژوهشگر ارشد

• Cyber Security
• Machine Learning
• Industrial Control System (ICS) Security
• +۳ مورد دیگر

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.