جستجوی استاد راهنمای دانشگاهی

Chadni Islamمشاهده پروفایل

مدرس

• Cyber Security
• Software Engineering
• Machine Learning
• +۵ مورد دیگر

Dr Chadni Islam is a Lecturer in the School of Computer Science at Queensland University of Technology (QUT), specializing in Cyber Security and Software Development. She holds a PhD from the University of Adelaide. Her research focuses on cybersecurity, software supply chain attacks, machine learning applications in security, and NLP techniques in software debugging. Dr. Islam has contributed to frameworks for post-quantum cryptography migration, dynamic threat hunting, and security tool orchestration. Her work includes developing datasets like QUT-DV25 for analyzing next-gen software supply chain attacks, and tools such as DySec for detecting malicious packages in the PyPI ecosystem. She has published extensively on topics like API misuse detection, system-level bug reproduction, and risk assessment frameworks. Dr. Islam’s research bridges theoretical advancements with practical cybersecurity challenges, emphasizing automation, tool integration, and threat intelligence. Her articles reflect a strong focus on real-world applications, including improving software supply chain resilience and enhancing security through machine learning.

Dakshi Kapugama Geeganageمشاهده پروفایل

پژوهشگر ارشد

• Text Mining
• Topic Modelling
• Natural Language Understanding
• +۳ مورد دیگر

Dr. Dakshi Kapugama Geeganage is a Research Fellow in Process Science and Sessional Academic at the School of Information Systems, Queensland University of Technology (QUT). She holds a PhD in Data Science from QUT (2022) and has prior academic qualifications including an MSc in Artificial Intelligence (University of Moratuwa, Sri Lanka, 2014) and a BSc (Special) Honors in Information Technology (Sri Lanka Institute of Information Technology, 2010). Her research focuses on text mining, topic modelling, natural language understanding, and process mining. She has contributed to advancements in semantic-based topic representation, event log enrichment using unstructured text (Text2EL+), and digital health data quality analysis. Her work bridges data science methodologies with practical applications in healthcare and process optimization. Key publications include studies on semantic-enhanced topic modelling (e.g., Semantic-LDA), digital health data imperfections, and event log enrichment. Her research trends emphasize integrating semantic analysis and domain-specific expertise to enhance data-driven decision-making. Awarded Fellow of the Higher Education Academy (FHEA), Dr. Geeganage is actively involved in teaching and research supervision. Her projects often involve collaborations with interdisciplinary teams, focusing on real-world challenges in healthcare data systems and process automation. She is affiliated with the QUT Centre for Data Science, contributing to initiatives in sports data science and AI.

Zhiyong Liمشاهده پروفایل

استاد

• Biomechanics
• Mechanobiology
• Hemodynamics
• +۷ مورد دیگر

Professor Zhiyong Li is a Professor in Biomedical Engineering at Queensland University of Technology (QUT), affiliated with the School of Mechanical, Medical and Process Engineering. He holds a PhD from Queen Mary University of London and a MA from the University of Cambridge, where he was a research fellow and later an elected Fellow of Wolfson College. His research focuses on biomechanics, mechanobiology, and cardiovascular diseases, particularly atherosclerosis and aneurysm. He has pioneered novel imaging and computational methods for assessing high-risk plaques, with over 100 publications in top journals such as Circulation and Nature Communications . Awards include the 2014 ARC Future Fellowship and the 2015 Australia Vascular Biology Society Award. Education: BEng in Civil Engineering (1998), Tongji University PhD in Biomedical Engineering (2004), Queen Mary University of London MA (2009) from University of Cambridge Research Interests: Mechanical and hemodynamic factors in atherosclerosis progression Development of MRI/IVUS/OCT-based plaque assessment tools Biomaterial scaffold design for bone regeneration Left atrial appendage thrombosis mechanisms in atrial fibrillation Computational modeling of vascular mechanics Articles Trends: His recent work (2024-2025) emphasizes advanced computational models for cardiovascular systems, novel biomaterials for tissue engineering, and innovative encryption algorithms for medical data security. Key themes include patient-specific biomechanical analysis, thrombus formation mechanisms, and scaffold design for bone regeneration. Scientific Awards: ARC Future Fellowship (2014) 2015 Australia Vascular Biology Society Achievement Award Advising & Grants: Currently supervising multiple PhD candidates funded by ARC and CSC scholarships, focusing on projects like plaque vulnerability assessment and scaffold design. Active in securing competitive grants for translational biomedical engineering research. Labs/Teams: Leads a multidisciplinary team in QUT's Centre for Data Science and collaborates with clinical partners on translational cardiovascular research initiatives.

• Aging and Gerontology
• Cardiovascular Disease
• Neurodegenerative Disorders (Dementia)
• +۴ مورد دیگر

Robyn L. Woods is an Associate Professor in the School of Public Health and Preventive Medicine at Monash University, based at the Alfred Hospital campus. She leads major clinical trials such as the ASPREE study, the largest Australian clinical trial involving 16,703 elderly participants. Her research focuses on aging, cardiovascular health, dementia, and the role of aspirin in preventing age-related diseases. She has secured significant grants from NIH and NHMRC, including funding for the ASPREE-XT extension and the ASPREE-Microbiome project. She has over 280 publications and an H-index of 37. A/Prof Woods has supervised 9 PhD and 6 Honours students, with 2 current PhD candidates. Research Interests: Her work addresses frailty, physical disability, cardiovascular disease, depression, and kidney function in older adults, with a focus on biomarkers and clinical guidelines. She has contributed to changing aspirin usage guidelines for older populations through landmark studies in the New England Journal of Medicine. Grants & Awards: Secured NIH and NHMRC grants for ASPREE and its extensions. Awards include the Howard Florey Medal (2005) for animal welfare and the National Heart Foundation's President's Award for peer review contributions. Projects: Key initiatives include the ASPREE Healthy Ageing Biobank, ASPREE-CHIP-XT, and collaborations on microbiome research in aging. Her work aligns with UN Sustainable Development Goals for health and well-being. Labs/Teams: Director of the ASPREE Healthy Ageing Biobank and collaborator on the NIH-funded ASPREE-Microbiome project. Leads multidisciplinary teams across clinical trials, biomarker analysis, and epidemiology.

• Machine Learning
• Neuroscience
• Neuroimaging
• +۳ مورد دیگر

Mohsen Ghofrani-Jahromi is a Research Fellow in Psychology at Monash University, actively engaged in interdisciplinary research bridging computational methods and clinical neuroscience. His research focuses on developing explainable machine learning frameworks for neurodegenerative disorders, particularly Huntington's disease. He specializes in prognostic enrichment strategies using neuroimaging data to identify early-stage biomarkers and optimize clinical trial design. His work integrates artificial intelligence with clinical psychology to address challenges in genetic disorder progression prediction. His 2024 publication in NeuroImage: Clinical demonstrates a trend toward AI-driven clinical neuroscience, applying interpretable machine learning to Huntington's disease data for improved patient stratification in trials. This reflects growing emphasis on computational approaches in understanding neurodegenerative disease trajectories. No scientific awards are documented in the available information. Dr. Ghofrani-Jahromi secured industry funding as Primary Chief Investigator for the 'Image Classification of Corrosion Cladding' project (2023) with AGL Energy Limited, though no student advising activities are referenced. His research portfolio shows emerging strength in cross-disciplinary applications of machine learning to both medical and engineering challenges.

• Secure Software Systems
• Cyber Security
• Cloud Computing
• +۵ مورد دیگر

Professor Ali Babar is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. He holds the academic rank of Professor and leads research in secure software systems, cyber security, and emerging technologies like cloud computing and IoT. Research focuses include engineering secure software systems, applying AI/ML for security, and empirical methods in software engineering. His work has resulted in over 320 peer-reviewed publications (h-index 66) and contributions to high-impact projects like the Cyber Security Cooperative Research Centre (CSCRC), a A$140M initiative. Collaborations span industry/government partners such as DST Group, ActewAGL, and Cisco. Awards include the 2014 Most Influential Paper Award at the Australasian Software Engineering Conference. His research addresses critical areas like secure microservices, ML model decay mitigation, and privacy engineering practices. Current themes include cyber security as a service and platform architectures for distributed systems. Active in supervising PhD and Master's students and participates in interdisciplinary initiatives like the Oceania Cyber Security Centre and Australian Cyber Collaboration Centre.

• Carbon nanomaterials
• Conducting polymers
• Nanocomposites
• +۳ مورد دیگر

Dr. Tung Tran is a Lecturer and ARC Grant-Funded Researcher (B) at the School of Chemical Engineering, University of Adelaide. He leads research in carbon nanomaterials, conducting polymers, and nanocomposites for sensor and biomedical applications. With a PhD from Korea University (2010), he has held postdoc positions at institutions including the University of South Brittany (France) and Strasbourg University. His work focuses on advanced material design for applications in environmental sensing, energy storage, and medical diagnostics. Key contributions include graphene/MOF hybrid sensors, nanocomposite piezoresistive materials, and functional coatings. He has secured over $1.8M in grants and published >100 SCI articles with an h-index of 42. Research interests span nanomaterial synthesis, functional coatings, and biomedical nanotechnology. His lab develops sensor technologies integrating machine learning for enhanced performance in detecting VOCs, pathogens, and cancer biomarkers. He explores novel applications of 2D materials for radiation shielding, energy storage systems (e.g., vanadium flow batteries), and anti-corrosion coatings. Grants: ARC Discovery, ARC Linkage, industrial partnerships Patents: 5 granted, focusing on nanomaterial fabrication and sensor designs Collaborations: Global networks in Europe, Asia, and Australia Supervision: Open to PhD/Masters candidates in materials science and engineering Recent work includes machine learning-enhanced chemiresistive sensors for toxic substance detection, magnetic nanowire-based RNA extraction systems, and superhydrophobic coatings for anti-icing applications. His research bridges nanotechnology with real-world challenges in healthcare, environment, and energy sectors.

• Cyber Security
• Big Data Analytics
• Software Engineering
• +۲ مورد دیگر

Dr. Faheem Ullah is an Assistant Professor in the School of Computer and Mathematical Sciences at the University of Adelaide. He holds a dual role as Lecturer/Assistant Professor and is part of the Centre for Research on Engineering Software Technologies (CREST), where he leads R&D in big data analytics and cloud computing. His research focuses on cybersecurity, big data analytics, software engineering, cloud computing, and machine learning. He has applied these skills in healthcare, oil & gas, defense, and green computing domains. Dr. Ullah is eligible to supervise Masters and PhD students as a co-supervisor, emphasizing collaborative research endeavors. He actively contributes to the design and evaluation of cybersecurity systems, including frameworks for microservices, distributed data processing, and adaptive security analytics. His work bridges theoretical advancements with industry applications, addressing challenges such as vulnerability analysis in microservices, AI-driven threat detection, and optimizing cloud infrastructure. He is a sought-after expert in cybersecurity and cloud-native technologies, with a strong publication record in top-tier venues.

• Probability Theory
• Spatial Point Processes
• Queueing Theory
• +۳ مورد دیگر

Dr Gopalan Nair serves as a Senior Lecturer in the Department of Mathematics and Statistics at The University of Western Australia's School of Physics, Maths and Computing. He joined UWA in 2008 after holding academic positions at Auburn University, The University of Queensland, Curtin University, UC Santa Barbara, and the National University of Singapore. His educational background includes: Bachelor's in Mathematics (with Statistics and Physics minors) from Calicut University, India Research scholarship at Tata Institute of Fundamental Research Centre, Bangalore PhD in Mathematical Statistics from the University of Melbourne (1989) Dr Nair's research centers on Probability Theory and Spatial Point Processes , with significant contributions to Queueing Theory and Financial Modeling . His work on point patterns on linear networks and combinatorial queueing methods bridges theoretical statistics with practical applications in spatial analysis and risk assessment. Recent publications emphasize regularization techniques (LASSO) in volatility modeling and spatial pattern analysis. His publication trends (2022-2025) reveal strong integration of statistical theory with computational algorithms, particularly in financial time series (ARCH/GARCH models) and earth sciences (mineral prospectivity analysis). These studies demonstrate cross-disciplinary applications in finance, geology, and road safety. Dr Nair has secured research funding for: Urban Monitor (D61 Challenge: E03) with CSIRO (2019, curtailed) Statistical Methodology for Events on Network Applications to Road Safety (ARC-funded, 2013-2015) He supervises Honours students as Departmental Coordinator and maintains an active collaboration as Visiting Scientist at CSIRO Floreat.

Fuyi Wangمشاهده پروفایل

پژوهشگر ارشد

• Privacy-Enhancing Technologies
• Secure AI Systems
• AI-Driven Business Intelligence
• +۵ مورد دیگر

Fuyi Wang is a Research Fellow in AI and Data Analytics at RMIT University's Accounting, Info Sys & Supply Chain department (City Campus, Australia). She holds a Ph.D. in Information Technology from Deakin University (expected 2025). Her research focuses on privacy-preserving technologies, secure AI systems, federated learning, and robust data security frameworks. Notable contributions include cryptographic solutions for medical data security, privacy-enhanced federated learning, and secure biometric systems. Her work has been published in top-tier venues such as IEEE TSC, TIFS, USENIX Security, and ICLR. Recent research themes emphasize backdoor attack mitigation in federated learning, trust-enhanced medical inference systems, and privacy-preserving algorithms for intelligent transport systems. She has held academic roles including Visiting Scholar at Singapore University of Technology and Design (2024–2025) and Casual Lecturer at Edvantage Institute (teaching data security, cryptography, and digital forensics). Collaborative projects include federated learning defense frameworks (e.g., FedCT, FedWARD) and cross-cluster privacy solutions. Her publications consistently address real-world challenges in secure AI deployment, with a focus on balancing functionality and privacy preservation in distributed systems. Education: Ph.D. Information Technology, Deakin University (2021–2025) Key Contributions: Over 15 peer-reviewed publications since 2022, with 2023–2025 work focusing on federated learning security and medical AI privacy Teaching: Courses in data security, cryptography fundamentals, and digital forensics at undergraduate and graduate levels Labs/Teams: Engaged in interdisciplinary research bridging AI, cybersecurity, and healthcare applications

• Cybersecurity
• Privacy-Preserving Machine Learning
• Secure Multi-Party Computation
• +۳ مورد دیگر

Xiaoning (Maggie) Liu is a Lecturer (equivalent to US Assistant Professor) at the School of Computing Technologies, RMIT University, where she earned her Ph.D. in Computer Science. Her research focuses on secure systems to address data privacy and security challenges in machine learning, particularly through secure multi-party computation (MPC) and privacy-preserving machine learning (PPML). She applies these techniques to practical domains like medical diagnostics and mobile image classification. Her design philosophy emphasizes lightweight, fundamental protocols leveraging advanced cryptographic methods, such as function secret sharing and zero-knowledge proofs. Research Interests: Secure multi-party computation and its applications in PPML Privacy-preserving medical diagnostics (e.g., MediSC , CryptMed , OblivGNN ) Secure cloud and edge computing systems ( Sonic , EncSIM , Leia ) Cross-domain encryption and collaborative computation Advising & Supervision: Xiaoning supervises Masters and PhD students in research projects such as intrusion detection systems for healthcare IoT devices, defenses against backdoor attacks in neural networks, and privacy-preserving fairness verification. She emphasizes recruiting self-motivated researchers passionate about impactful security work. Interdisciplinary Contributions: Her work bridges computer systems, cryptography, and machine learning to address real-world challenges in areas like MLaaS, medical data collaboration, and mobile-edge inference. Collaborative frameworks like MediSC and Leia

Malka N. Halgamugeمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• AI Security
• Large Language Models
• +۵ مورد دیگر

Dr. Malka N. Halgamuge is a Senior Lecturer in Cybersecurity at RMIT University, Melbourne, Australia, and Chair of the IEEE Computational Intelligence Society Victorian Section. She holds a PhD from the University of Melbourne and has over 5,200 citations with an h-index of 38. Her research focuses on cybersecurity, AI security, large language models, blockchain, and IoT. Previously, she worked at La Trobe University as a Senior Lecturer and Course Coordinator for Micro-credential Cybersecurity courses. She managed the Australian Government's $4.7M Cyber Security Skills Partnership Innovation Fund, collaborating with industry partners like Cisco and Optus. Her industry experience includes roles as an EMR consultant, founder of SenseRadiation Pty Ltd, and part-time algorithm researcher at GreenBox Services. Malka has led over 300 conferences, including roles as Program Co-Chair and Track Chair. She has received numerous awards, including the CAS President's Fellowship and recognition as a top 2% cited researcher globally. Her work spans academic research, industry collaboration, and policy engagement in cybersecurity and emerging technologies.

• Coding Theory
• Discrete Mathematics
• Blockchains
• +۵ مورد دیگر

Dr. Son Hoang Dau is a Senior Lecturer in Computer Science at the School of Computing Technologies, RMIT University, Australia. His research focuses on coding theory, discrete mathematics, blockchains, and their applications in secure distributed systems and quantum computing. He actively supervises PhD and Master’s students in areas like fraud detection on blockchain networks and quantum annealing for satellite communication optimization. Research Interests: - Coding Theory & Error-Correcting Codes - Blockchain Security & Decentralized Systems - Quantum Computing & Optimization - Privacy-Preserving Algorithms - Distributed Storage Systems Dr. Dau's recent work includes groundbreaking studies on blockchain fraud detection, secure erasure coding in distributed storage, and quantum annealing for satellite beam placement. His contributions bridge theoretical computer science with practical applications in cybersecurity and telecommunications. Supervision Projects: - Machine Learning methods for Fraud Detection on Blockchain networks - Efficient Repair of Reed-Solomon Codes - Robust Computer vision for surveillance and threat detection Collaboration Network: Engages with global researchers in cryptography, distributed systems, and quantum computing through publications in top venues like IEEE Transactions and ACM Conferences.

Reza Hoseinnezhadمشاهده پروفایل

استاد

• Electrical and Electronic Engineering
• Artificial Intelligence and Image Processing
• Mechanical Engineering
• +۳ مورد دیگر

Professor Reza Hoseinnezhad is a faculty member in the School of Engineering at RMIT University, Australia. His research focuses on advanced engineering systems, including robotics, artificial intelligence, and autonomous systems. His work spans domains such as multi-object tracking, sensor fusion, and control systems with applications in underwater vehicles, autonomous driving, and manufacturing. He actively supervises research projects in areas like electronic seatbelt systems, anomaly detection, and swarm tracking. Research interests include Electrical and Electronic Engineering, Artificial Intelligence, Mechanical Engineering, and Manufacturing Engineering. His contributions leverage statistical methods, machine learning, and optimization to solve complex engineering challenges. Recent projects emphasize robust filtering, adversarial attack defenses, and distributed information fusion in connected systems. Professor Hoseinnezhad’s publications address cutting-edge topics like geometrically-informed particle filters, reinforcement learning for quadrupedal robots, and defect detection via point pattern analysis. His work bridges theoretical advancements with practical industrial and safety applications.

• Computation Theory and Mathematics
• Distributed Computing
• Information Systems
• +۷ مورد دیگر

David Taniar is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. He holds a PhD in Computer Science from Victoria University (1997), and prior degrees from Swinburne University of Technology. His research focuses on computation theory, distributed systems, health informatics, and AI applications in healthcare. Taniar has led/co-investigated over 15 projects including digital health interventions, eco-friendly navigation systems, and healthcare data analytics. Current projects include a 2023-2026 initiative on pathology requesting practices in Emergency Departments. He has published 508+ works, spanning medical image analysis, spatial data processing, and cybersecurity. Awards include the 2021 Good Design Award for a hand hygiene management system. Education: Doctor of Philosophy, Computer Science, Victoria University (1997) Master of Applied Science, Computer Science, Swinburne University (1992) Graduate Diploma, Computer Science, Swinburne University (1990) Research Focus: Combines AI/ML with domain-specific challenges in healthcare, transportation, and distributed systems. Recent work addresses disease detection via medical imaging (e.g., glaucoma, diabetic ulcers), spatiotemporal data optimization for eco-routing, and ED data utilization. Key Projects: A digital health intervention to improve pathology requests in Emergency Departments (2023-2026) Eco-friendly navigation using big spatiotemporal data (2023) Actionable clinical data systems for quality care accreditation (2021-2025) Awards & Activities: Recipient of 2021 Good Design Award for healthcare innovation Editor-in-Chief of International Journal of Business Data Communications and Networking (2007) Chief Examiner for multiple IT courses at Monash