جست‌وجوی استاد و پژوهشگر

Paul Wongمشاهده پروفایل

مدرس ارشد

• Cybernetics
• Complex Systems Science
• Network Science
• +۴ مورد دیگر

Paul Wong is a Senior Lecturer at the School of Cybernetics of the Australian National University (ANU). He holds a PhD in Non-classical Logics from ANU (2004) and a Master's in Philosophy from Simon Fraser University (1996). His academic career includes roles as a lecturer in software engineering, policy analyst, and defense analyst. His research focuses on cybernetics, complex systems science, network science, and data-driven decision-making. He has contributed to national research infrastructure development and strategic data management practices. Research Interests: Cybernetics and sociotechnical systems design Complex systems modeling and analysis Network science applications in research evaluation Data quality and integration methodologies Policy analysis using predictive analytics Recent work emphasizes leveraging data networks to forecast research impacts and optimize national innovation strategies. His 2022 publications address cybernetic frameworks for complex world design and non-academic research impacts. He has co-authored over 13 peer-reviewed works across data science, logic, and interdisciplinary systems analysis. Professional Contributions: Principal Investigator on AI Library Services and Research Performance Evaluation projects Member of ACM and IEEE since 2024 Labs/Teams: Active in ANU's School of Cybernetics research community focusing on interdisciplinarity and systems thinking.

Mehwish Nasimمشاهده پروفایل

مدرس

• Social Cybersecurity
• Information Warfare
• Combating Misinformation
• +۹ مورد دیگر

Mehwish Nasim is a Lecturer in Computing and Mathematical Sciences at the College of Science and Engineering, Flinders University, Australia. She holds a PhD in Computer Science from the University of Konstanz, Germany, and is actively engaged in research at the intersection of computer science and social dynamics, particularly in the domain of information integrity. Her educational background includes: PhD in Computer Science, University of Konstanz, Germany Master of Science in Software Engineering, College of Electrical and Mechanical Engineering (NUST) Bachelor of Science (Honours) in Computer Science, National University of Computer and Emerging Sciences Mehwish's research focuses on social cybersecurity , aiming to detect, analyze, and counter malicious communication strategies on digital platforms. She applies interdisciplinary methods from network analysis, machine learning, natural language processing, and agent-based modeling, drawing insights from psychology and social sciences. Her work investigates who manipulates online discourse, what techniques they use (e.g., misinformation campaigns), and how such threats can be mitigated. She leads the Network Analysis & Social Influence Modeling (NASIM) Lab , where she develops computational frameworks to study influence and manipulation in social systems. Her research extends to cyber security decision-making, health analytics, and designing technology for underserved user populations. Mehwish is also a recognized media expert and public speaker, having delivered a TEDx talk. She contributes to public discourse on data mining, information technology, and strategies to combat misinformation. She serves as topic coordinator for several courses, including: COMP8031 / COMP2031 – Data Engineering COMP8711 / COMP1711 – Database Modelling and Knowledge Engineering She is available for media and research inquiries via email.

• Mechanical Engineering
• Dynamic Systems
• Signal Processing
• +۱۰ مورد دیگر

Md Imam Hossain is a Tutor at the Faculty of Engineering & Information Technology , University of Technology Sydney. He holds a Bachelor of Mechanical Engineering and a Master of Engineering (Research) from UTS. His work focuses on mechanical design, dynamic systems, and biomechanics applications in sports safety. Education : B.Mech.Eng., M.Eng.(Res) (UTS) Teaching : Courses in Mechanical Vibration, Dynamic Systems, and Control Research Areas : Mechanical design, signal processing, sports biomechanics, impact attenuation, simulation modeling Hossain's research integrates mechanical engineering with biomechanics , particularly in greyhound racing and trampoline dynamics . His publications demonstrate expertise in sensors , numerical modeling , and sports infrastructure optimization . Key article trends include: Quantifying g-forces and injury risks in greyhound racing Dynamical modeling of trampoline park safety Simulation platforms for multi-variable motion analysis Software-defined network implementations Acceleration and jerk analysis in animal locomotion Impact attenuation material testing He contributes to industry projects through funded research in system automation , data acquisition , and software development for mechanical systems.

David Broadhurstمشاهده پروفایل

استاد

• Metabolomics
• Computational Biology
• Biostatistics
• +۴ مورد دیگر

David Broadhurst is Professor of Data Science & Biostatistics at Edith Cowan University's School of Science. With a background in Electronic Engineering, Medical Informatics, and Metabolomics, he has over 20 years of experience in systems biology, machine learning, and translational medicine. His career spans institutions including the University of Wales, University of Manchester, Cork University Maternity Hospital, and University of Alberta. His educational credentials include a PhD in Metabolic Profiling from Wales (1998), MSc in Medical Informatics from England (1993), and BEng in Electronic Engineering from England (1992). Research interests focus on integrative metabolomics and computational biology. Key themes include multi-omics data fusion, personalized population stratification through evolutionary computation, artificial neural networks for biomarker discovery, and data visualization techniques. Recent projects address precision medicine applications in pregnancy disorders, respiratory conditions, and critical care. The 15 most recent publications highlight metabolomic approaches to precision medicine, asthma phenotyping, probiotic therapy validation, and methodological advancements in LC-MS and NMR metabolomics. Articles emphasize interdisciplinary collaboration, clinical translation, and machine learning integration for multi-omic analysis. As Director of the International Metabolomics Society, he leads community initiatives for metabolomic epidemiology standards and quality assurance protocols. Supervision roles include PhD and Master's students in metabolomic profiling of gut microbiome and kidney disease biomarkers. Research funding encompasses grants for asthma prediction systems (2023-2025), prostate cancer microbiome studies (2018-2024), and infrastructure development for metabolic phenotyping. His work bridges computational methods with clinical applications across maternal health, respiratory disease, and critical care medicine.

Ong Huey Fangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Data Mining
• Bioinformatics
• +۷ مورد دیگر

Dr. Ong Huey Fang is a Senior Lecturer in the School of Information Technology at Monash University Malaysia since 2019. She holds a PhD in Intelligent Computing from Universiti Putra Malaysia, with earlier degrees from Universiti Teknologi Malaysia. Her research focuses on artificial intelligence applications in bioinformatics (e.g., cancer biomarker discovery), machine learning (transfer learning for Malaysian English), and computer vision (micro-expression analysis). She has led projects like the 2022 Deep Associative Classification initiative. Professional memberships include IEEE, ACM, and AWS certification. Current work integrates multi-omics data analysis and causal graph modeling to address biases in AI systems. Education: BSc (UTM), MSc & PhD (UPM) Professional Certifications: AWS Cloud Practitioner Research spans cancer biomarkers via omics data, NLP for low-resource languages, and VR-based educational tools. Recent projects include causal analysis of video micro-expression bias and blockchain-enhanced healthcare systems. She actively collaborates internationally on topics like biomedical event extraction and supply chain digitalization.

Nik Nailahمشاهده پروفایل

مدرس

• Multi-Agent Systems
• Medical Informatics
• Healthcare AI
• +۴ مورد دیگر

Dr. Nik Nailah is a Lecturer at the Malaysia School of Information Technology, Monash University. Her research focuses on applying cognitive science and multi-agent systems to develop cost-effective AI solutions for underserved populations, particularly in healthcare. She specializes in creating tools for rural patients with chronic illnesses like heart failure and cancer. Notable contributions include the MARIA agent system for medication adherence and frameworks for standardizing clinical data using FHIR. Key Awards: Recipient of the Pro Vice Chancellor Award for Excellence in Education (2015) for pioneering project-based learning initiatives. Also recognized with the Ke Arah Komuniti Bebas Diabetes Prize (2021) for community-focused diabetes management systems. Research Impact: Her work has been featured in STAR newspaper for analyzing hospital systems and improving human-computer interface designs. She has spoken globally, including at Google Tech Talks (2008) and the 'Technovisionaire' event in Milano (2009), where she was a finalist for the Women and Technology Award. Current Projects: Leading three major initiatives including biometric cryptosystems for security, deep learning for child behavior analysis, and intelligent medicine frameworks. Her research addresses UN Sustainable Development Goals, particularly in health and technology access. Collaborations: Active in international projects with institutions in Malaysia, China, Australia, and Europe, focusing on AI ethics, healthcare informatics, and secure communication protocols.

• Natural Language Processing
• Mental Health
• Text Clustering
• +۳ مورد دیگر

Siti Nabilah Mohd Abdul Hakim Amir is an Assistant Lecturer at the Malaysia School of Information Technology, Monash University. She holds a Master of Information Systems (2023–2024) and a Bachelor of Information Systems (2019–2021), both from Universiti Teknologi MARA (UiTM). Her research focuses on natural language processing applied to mental health analysis, particularly during the pandemic, and text clustering techniques for healthcare and consumer data. Education: Master’s: Cluster Analysis on People’s Mental Health During COVID-19, UiTM (2021–2023) Bachelor’s: Restaurant Reviews Using Text Clustering, UiTM (2019–2021) Her academic background combines industry experience as a software engineer and junior developer, contributing to travel app development. She previously taught C programming part-time at an institute. Siti’s work bridges academic research in data analysis with practical applications in mental health and consumer analytics.

Jeffrey Chanمشاهده پروفایل

استاد

• Machine Learning
• Recommender Systems
• Responsible AI
• +۶ مورد دیگر

Jeffrey Chan is a Professor and Associate Dean of Data Science & Artificial Intelligence at RMIT University's School of Computing Technologies. His research focuses on machine learning, recommender systems, responsible AI, and interdisciplinary applications in sustainability, healthcare, and transportation. He has held roles such as Assistant Associate Dean and led projects on fairness-aware systems, energy-efficient recommendations, and AI-driven circular economies. His work bridges theory and practice, collaborating with industry and non-profits. He teaches courses in algorithms, network analytics, and machine learning, and actively supervises PhD and Honours students. Notable contributions include grants from the Australian Research Council and a Best Paper Award at ACM International Conference on Web Science 2011. His research also addresses ethical challenges in AI, such as fairness and transparency, and explores mobility solutions like e-scooter dynamics and parking optimization. Research Interests: Machine Learning, Recommender Systems, Data-Driven Optimization, Social Network Analysis, Responsible AI, Cloud Security, and Sustainability. Awards: Best Paper Award at ACM International Conference on Web Science 2011 Grants: Australian Research Council Discovery Project (2017–2019), Early Career Researcher Grant (2015), and EU FP7 ROBUST project (2011–2013). Labs/Teams: Leads initiatives in AI ethics, mobility systems, and interdisciplinary data science collaborations.

Asif Karimمشاهده پروفایل

مدرس

• Machine Learning
• Health Informatics
• Blockchain Applications
• +۷ مورد دیگر

Dr. Asif Karim is a Research Active Lecturer in the Department of Information Technology at the Faculty of Science and Technology, Charles Darwin University, Australia. He has been a full-time lecturer since August 2021, following a sessional role from 2018 to 2021. Prior to his current position, he served as a lecturer at Daffodil International University and Uttara University in Bangladesh. His research focuses on the application of machine intelligence in health informatics and blockchain technologies. He has significant industry experience in Software Engineering and actively supervises postgraduate research students. Machine Learning Health Informatics Blockchain Applications Smart Contracts Deep Learning Anomaly Detection The recent publications of Dr. Karim span a diverse range of applications in artificial intelligence, particularly in healthcare and secure computing. His work includes developing efficient deep learning models for medical diagnosis, privacy-preserving classification of diseases from medical images, and anomaly detection in cybersecurity. He also explores applications in mobile cloud computing and agricultural technology, demonstrating a broad interdisciplinary approach to solving real-world problems using machine learning. Dr. Karim actively contributes to research projects and supervises postgraduate students. He led the project "Machine Learning Diagnostics System for Bronchiectasis" and has extensive experience in teaching undergraduate and postgraduate courses in computer science, including Machine Learning, Operating Systems, and Software Engineering. He is involved in organizing academic events, such as a digital awareness workshop for rural indigenous communities, reflecting his commitment to community engagement and technology outreach.

• Software Engineering
• Human-Centred Computing
• Information Systems
• +۴ مورد دیگر

Tanjila Kanij is a Lecturer at the School of Science, Computing and Emerging Technologies , Swinburne University of Technology, with an ORCID identifier 0000-0002-5293-1718 . Her research focuses on gender inclusion in software engineering, user-centred design , and human-computer interaction . Research Themes : Software Engineering, Human-Centred Computing, Information Systems Teaching : Final-year Capstone project units Her recent publications address ethical challenges in Generative AI , gender bias in IT/SE job ads , and privacy-awareness applications . She explores persona development for diverse user groups like children and the elderly, and knowledge graph construction for fisherfolk communities. She is funded by the Digital Shores grant (2024-2025) from the Department of Foreign Affairs and Trade (Cwlth), focusing on empowering coastal communities through technology.

Leibo Liuمشاهده پروفایل

پژوهشگر ارشد

• health data science
• artificial intelligence
• machine learning
• +۳ مورد دیگر

Leibo Liu is a Research Fellow at the Centre for Big Data Research in Health (CBDRH) at the University of New South Wales (UNSW) Sydney and a Data Scientist at The George Institute for Global Health (TGI). Education: PhD in Big Data Research in Health from Centre for Big Data Research in Health (CBDRH), University of New South Wales (UNSW), Australia Research Interests: Liu specializes in applying natural language processing (NLP) and deep learning to extract information from unstructured health data, particularly clinical notes. His key research areas include de-identification and clinical coding of free-text data from electronic medical records (EMRs), and predictive modeling using brain imaging data with convolutional neural networks and transformer-based architectures. He developed an end-to-end deep learning framework for removing personally identifiable information (PII) from health data, implemented in the Cardiac Analytics and Innovation (CardiacAI) project. Current Projects: Next-generation Clinical Registry project at CBDRH Outcome PredicTion in IntraCerebral Hemorrhage Study (OPTICS) at TGI (leads machine learning model development) Professional Background: With expertise spanning machine learning, software engineering, and big data analysis, Liu brings a unique combination of skills to health data science. His publication record includes 1 Book Chapter, 9 Journal articles, 1 Conference Paper, 1 Conference Abstract, and 6 Preprints.

Dadong Wangمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۴ مورد دیگر

Dr Dadong Wang is a Professor at the University of New South Wales (UNSW) and a Senior Principal Research Scientist & Leader of the Imaging and Computer Vision Research Group (ICVG) at CSIRO Data61. His group, with over 70 members across Brisbane, Canberra, and Sydney, specializes in end-to-end imaging and computer vision solutions for scientific and industrial applications. He also holds an adjunct Professor position at the University of Technology, Sydney (UTS). Research Interests: Image Analysis, Computer Vision, Artificial Intelligence, Machine Learning, Signal Processing, Software Engineering Key Contributions: Leadership in ICVG, development of intelligent systems for monitoring and control in multinational companies (2005-2011), extensive publications (over 150), and industry collaborations for technology commercialization. Scientific Recognition: Recipient of multiple awards including APICTA, iAwards, CSIRO Performance Cash Rewards, and Engineering Excellence Industry Award by Engineers Australia (2009). Dr Wang actively recruits PhD candidates in Image Analysis, Video Analytics, Computer Vision, Deep Learning, Augmented Reality, and Virtual Reality. His work bridges theoretical advancements with practical applications in multi-dimensional image data.

Eric Howardمشاهده پروفایل

پژوهشگر ارشد

• Quantum Physics
• Machine Learning
• Gravitational Waves
• +۴ مورد دیگر

Dr. Eric Howard is a Research Fellow at Macquarie University , affiliated with the School of Engineering , School of Mathematical and Physical Sciences , and School of Computing . His research spans interdisciplinary domains at the intersection of quantum physics, machine learning, and AI-driven systems. Key research themes include: Quantum cryptography for Industry 4.0 security Machine learning in IoT temperature sensing Adversarial AI in cybersecurity 6G wireless communication optimization Quantum information processing Deep learning for data imputation Recent publications demonstrate a focus on emerging technologies, with articles on quantum Bayesian inference , 6G signal processing , and smart city IoT systems . His collaborative work extends to blockchain-enabled supply chain visibility and generative AI applications in programming. Research collaborations span institutions in India (AIP Publishing) and Australia, with technical contributions to quantum dynamics, neural network applications, and nanosensor development.

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +۵ مورد دیگر

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

Fethi Rabhiمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence Systems Design
• FinTech
• +۶ مورد دیگر

Professor Fethi Rabhi is affiliated with UNSW Sydney as a faculty member of the School of Computer Science and Engineering . His role combines research in software engineering, AI systems design, and FinTech with teaching and fostering university-industry collaborations in financial applications. Research Focus : Software systems, AI design, financial technology, semantic data, and service-oriented computing. Grants : Secured ~$4M in grants across Fintech AI, Smart Services CRC, Ad-hoc Data Grids, and Capital Markets CRC projects. Teaching : 30 years of experience in software engineering, bridging IT and finance disciplines. Leadership : Co-founded the Fintech AI Innovation Consortium (FAIC) in 2022.