جست‌وجوی استاد و پژوهشگر

Mark Uttingمشاهده پروفایل

دانشیار

• Software Verification
• Model-Based Testing
• Formal Methods
• +۵ مورد دیگر

Associate Professor Mark Utting is affiliated with the University of Queensland (UQ), specifically within the School of Electrical Engineering and Computer Science. He holds the rank of Associate Professor and focuses on software verification, model-based testing, and programming language design. His academic journey includes roles at multiple Queensland universities, Waikato University (NZ), and the University of Franche-Comte (France). He earned his PhD from UNSW on object-oriented language semantics. Utting has authored over 80 publications and the book 'Practical Model-Based Testing: A Tools Approach.' His current research emphasizes verifying compilers, blockchain smart contracts, ARM64 binaries, and AI-generated code. Key projects include the Algorand Centre for Sustainability Informatics and the BASIL project on secure information-flow logics. His research spans formal methods, theorem proving, and software engineering. Utting leads supervision in areas like compiler verification and smart contract analysis, with active involvement in grants such as the 'Directed and Incremental Analysis for DevSecOps' project. Utting collaborates on interdisciplinary projects, including energy grid simulations and cybersecurity strategies. His work bridges academia and industry, with past experience in genomics and manufacturing software development.

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.

Erick Alfons Lisanganمشاهده پروفایل

مدرس

• Intelligent Transportation Systems
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Erick Alfons Lisangan is a Computer Science lecturer at Atma Jaya University of Makassar and a Doctor of Philosophy student at the University of Western Australia's School of Physics, Mathematics and Computing. His research integrates artificial intelligence with transportation infrastructure to address urban mobility challenges. Education Master of Computer Science, Gadjah Mada University (2013-2015) Bachelor of Computer Science, Atma Jaya University of Makassar (2006-2010) Research Interests His core focus lies in Intelligent Transportation Systems , analyzing graph traffic networks and spatiotemporal data collection. He develops algorithms for real-time traffic condition inference using embedded systems and AI-driven simulation. Specialized work includes deep learning architectures (LSTM/BiLSTM) for passenger flow prediction, fuzzy logic systems for dynamic route optimization, and wireless sensor networks for smart city applications like flood detection. His research bridges theoretical AI with practical urban infrastructure solutions. Research Output Trends Publications (2018-2025) reveal a trajectory from foundational smart city sensor networks toward advanced AI-driven transportation modeling. Recent work emphasizes hybrid deep learning for Transjakarta bus system prediction, while earlier contributions established fuzzy ant colony systems for traffic-responsive routing and image-based fake news detection frameworks. Advising and Grants Lisangan serves as a casual tutor for Data Structures & Algorithms and Computational Thinking with Python at UWA during his PhD studies. No formal advisees or research grant details are documented in the provided materials. Labs and Teams His doctoral research operates under supervision of Professor Rachel Cardell-Oliver and Professor Amitava Datta at UWA, contributing to intelligent transportation systems research groups with cross-institutional collaborations in Indonesia and Australia.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +۵ مورد دیگر

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.

• Machine Learning
• Materials Science
• Nanoparticle Materials
• +۳ مورد دیگر

Amanda Parker is a Researcher in the School of Computing at the Australian National University (ANU). Her work focuses on applying machine learning to materials science and nanotechnology, with particular emphasis on nanoparticle design, multi-dimensional materials modeling, and computational chemistry. She has supervised research students and contributed to projects such as the 'Computing for Social Good Seed Grants 2023,' exploring efficient high-performance computing methods. Her research interests span machine learning algorithms, data-driven materials design, and the computational analysis of nanomaterials. Recent projects include developing graph-based representations for multi-dimensional materials and optimizing active learning strategies for scientific simulations. She has published extensively on topics like nanoparticle classification, interfacial informatics, and the prediction of material properties using Bayesian inference and neural networks. Her work bridges computational methods with experimental insights, aiming to accelerate the discovery of novel materials for energy, catalysis, and environmental applications. Current efforts emphasize minimizing experimental bias and enhancing computational efficiency through advanced machine learning techniques.

Hanna Suominenمشاهده پروفایل

استاد

• Machine Learning
• Health Informatics
• Natural Language Processing
• +۴ مورد دیگر

Prof Hanna Suominen is a Professor of Computer Science and Associate Director (Neuroinformatics) at the Australian National University (ANU), and Co-Chair of the ANU Our Health in Our Hands strategic initiative. She holds a PhD and Adjunct Professorship from the University of Turku (Finland), and a Master of Leadership in Curriculum and Pedagogy from Monash University. Her research focuses on applying machine learning and data analytics to healthcare, particularly in neuroinformatics, precision medicine, and medical technology. She leads projects in AI-assisted workflows, chronic disease management, and medical imaging analysis. Notable contributions include developing tools for Parkinson's disease diagnosis via EEG, diabetes glucose prediction systems, and NLP-based medical information extraction. She has received the Senior Fellow of the Higher Education Academy (SFHEA) and has led teams at CSIRO/Data61. Her work bridges technology, education, and health to enhance Australia's healthcare innovation. Education: PhD in Computer Science (University of Turku, 2009) Adjunct Professorship (University of Turku, 2013) MSc in Applied Mathematics (University of Turku, 2005) MEdL in Curriculum & Pedagogy (Monash University, 2020) Research Interests: Machine learning applications in healthcare, neuroinformatics, medical imaging analytics, personalized medicine, natural language processing for health data, and AI ethics in medical technology. Her work emphasizes interdisciplinary collaboration with industry partners like Lightcast and Konan Medical. Grants & Projects: Leads ANU initiatives like the John Eccles Institute and OHIOH, with current projects including closed-loop brain stimulation for depression, AI-driven diabetes management systems, and biomarker discovery for multiple sclerosis. Active in securing funding for AI in advanced materials and healthcare informatics. Awards: Senior Fellow of the Higher Education Academy (ANU, 2019) Labs/Teams: Former leader of the TAMPA (Multimodal Pattern Analysis) and NLP teams at CSIRO/Data61. Currently oversees neuroinformatics research at ANU's John Eccles Institute.

• Graph Theory
• Combinatorics
• Mathematical Analysis
• +۲ مورد دیگر

Alex Newcombe serves as a Lecturer in the College of Science and Engineering at Flinders University, where he also holds a Research Staff position with the Centre for Defence Engineering Research and Training. His academic profile shows strong connections between theoretical mathematics and practical defense applications. His educational background includes: PhD in Mathematics from Flinders University (2019) Bachelor of Science with First Class Honours in Mathematics from Flinders University (2015) Newcombe's research centers on graph theory and combinatorics, with particular expertise in domination problems, crossing numbers, and Cartesian graph products. His work bridges theoretical mathematics with practical applications in defense and geolocation systems through collaborations with the Centre for Defence Research and Training. His publications demonstrate sophisticated approaches to complex graph structures using methods like cross-entropy optimization and binary programming formulations. Analysis of his recent publications reveals a consistent research trajectory focused on domination variants and graph product structures. His work shows increasing sophistication in handling complex graph problems with applications to network security and optimization. The publications span specialized mathematics journals including Entropy, Journal of Combinatorial Mathematics and Combinatorial Computing, and Ars Mathematica Contemporanea. His notable achievements include: Vice Chancellor's Award for Doctoral Thesis Excellence (2019) Participation in the Artificial Intelligence for Decision Making Initiative through the Defence Innovation Partnership (2020-2022) Newcombe teaches several engineering mathematics courses including ENGR2711 Engineering Mathematics, MATH3703 Optimisation, and ENGR8761 Engineering Mathematics. His research grants indicate strong institutional support for his work at the intersection of theoretical mathematics and defense applications. The Centre for Defence Engineering Research and Training serves as his primary research hub, facilitating collaborations between academic mathematics and practical defense challenges. His research activities are centered at the Tonsley campus of Flinders University, working within the College of Science and Engineering's research ecosystem. The Centre for Defence Engineering Research and Training provides the institutional framework for his defense-related collaborations, connecting mathematical theory with real-world security applications.

• Computational Biomechanical Engineering
• Heat and Mass Transfer
• Microfluidics Modelling
• +۶ مورد دیگر

Dr. Suvash C. Saha is a Senior Lecturer at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS), Australia. His academic journey includes a PhD in Computational Fluid Dynamics from James Cook University and postdoctoral training at Queensland University of Technology. Research Focus: Computational Biomechanical Engineering, Heat & Mass Transfer, Microfluidics Modeling Key Applications: Lung surfactant particle interactions, Red Blood Cell dynamics, Thermal energy systems His recent publications demonstrate expertise in PEM water electrolyzers, natural convection systems, and microplastic inhalation risks. The work spans energy systems optimization, biomedical applications, and environmental health impacts. Dr. Saha's research has been recognized through Stanford University's Top 2% Scientists (2023) and Research.com Mechanical & Aerospace Engineering rankings (2025). His team actively explores: Computational modeling of respiratory and cardiovascular flows Thermal management using phase change materials Multiscale simulations from cellular to organ-level systems Microplastic-nanoparticle interactions in pulmonary systems Scientific Awards: Stanford's Top 2% Scientists (2023) in career-long and single-year categories Research.com Top Mechanical & Aerospace Scientist #86 in Australia (2025) Australia-India Strategic Research Fund Fellowship As lab director of the Computational Biofluid and Heat Transfer (CBHT) Lab, he supervises PhD students in advanced CFD modeling, thermal systems analysis, and biomedical engineering applications using ANSYS Fluent, COMSOL, and OpenFOAM.

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.

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.

• 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.

Shuang Aoمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Reinforcement Learning
• Curriculum Learning
• +۴ مورد دیگر

Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Dr. Alan Blair is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales. His research spans artificial intelligence, machine learning, and neural networks, with applications in medical imaging, evolutionary computation, and cybersecurity. Key research areas: Artificial Intelligence, Machine Learning, Neural Networks, Evolutionary Computation, Medical Imaging, Computational Linguistics Recent publications focus on reinforcement learning for medical diagnosis, dialogue act tagging, and phishing detection using feature-free tools. Collaborations include work with Sowmya A, Iyer S, and Dawes L on 3D organ localization and fracture detection in CT scans. Blair's work integrates hybrid neural-logical reasoning models for visual question answering and hierarchical evolutionary algorithms for optimization tasks. He has contributed to adversarial image generation and game theory applications.

Xiao Chengمشاهده پروفایل

مدرس

• Programming Languages
• Software Engineering
• Software Security
• +۶ مورد دیگر

Xiao Cheng is a Lecturer in the School of Computing at Macquarie University, specializing in the intersection of Programming Languages and Software Engineering. His research focuses on enhancing software security and reliability through advanced analysis techniques. PhD in Computer Science and Engineering from the University of New South Wales Research interests include: Abstract interpretation and typestate analysis Integration of AI technologies like graph neural networks Malware detection in Android systems using DNN Recent publications address challenges in: Quantum search-optimized static analysis Dynamic malware label noise mitigation Recursion handling through topological ordering Scientific recognition includes: FSE 2024 ACM SIGSOFT Distinguished Paper Award OOPSLA 2020 ACM SIGPLAN Distinguished Paper Award Professional service includes: Web Chair for LCTES 2024 PC member for FSE 2026, ISSRE 2025, PAKDD 2025 Artifact Evaluation Committee for ICSE 2025 and ISSTA 2024

Helia Farhoodمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Dr. Helia Farhood is an Honorary Senior Research Fellow at the School of Computing, Macquarie University, specializing in Artificial Intelligence, Machine Learning, and Image Processing with applications in educational technology and object recognition. Her academic qualifications include a PhD in Computer Systems and Artificial Intelligence from the University of Technology Sydney (awarded November 2021) and a Master's degree in Computer-AI from Amirkabir University of Technology (Tehran Polytechnic, awarded September 2013). Dr. Farhood's research spans interdisciplinary AI applications, with significant contributions in student outcome prediction using generative adversarial networks, explainable AI through LIME heatmaps, and image-based storytelling systems. Her work integrates machine learning with educational data mining to enhance creativity assessment and learning analytics, while maintaining strong technical focus on 3D reconstruction and object recognition. Analysis of her 16 publications (2020-2025) reveals three dominant research trajectories: (1) AI-driven educational analytics for student performance prediction, (2) advanced image processing techniques for object recognition and 3D reconstruction, and (3) systematic reviews establishing methodological foundations in presentation attack detection and image-based storytelling. Her recent work increasingly emphasizes explainability and ethical considerations in AI deployment. Dr. Farhood has participated in externally funded research projects, including the 2022 project "Estimating the Number of Tyres in Stockpiles" (October-December 2022). No information is available regarding students she has advised. No information is available about specific research laboratories or teams led by Dr. Farhood.