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.
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
Kai Gao is a Vice Chancellor’s Postdoctoral Research Fellow (Level A) at the School of PCPM, RMIT University. His research focuses on urban climate mitigation, climate change adaptation, and thermal comfort in built environments. Key areas include radiative cooling technologies, green infrastructure, HVAC systems optimization, and the impact of urban planning on heatwaves. Dr. Gao’s work addresses interdisciplinary challenges such as balancing energy efficiency with thermal comfort in high-rise housing, evaluating the efficacy of tree-planting initiatives, and leveraging machine learning for anomaly detection in climate data. His research often combines experimental and numerical methods, with applications in tropical and arid urban settings like Hong Kong and desert cities. No scientific awards or grants are explicitly mentioned in the provided texts. His Google Scholar profile indicates active publication in urban climate science and related engineering fields.
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
Dr Lee How Chinh is a Senior Lecturer (Practice) in the Department of Econometrics and Business Statistics at the School of Business, Monash University Malaysia. He is actively involved in teaching, research, and industry collaboration, particularly in data science and analytics. Education: PhD in Statistics, Universiti Sains Malaysia Master of Applied Statistics, Universiti Putra Malaysia Master of Science in Mathematics, Universiti Kebangsaan Malaysia Bachelor of Science (Hons) in Mathematical Sciences, Universiti Kebangsaan Malaysia Dr Lee's research centers on applying statistical and machine learning methods to business and industrial problems. His key interests include predictive modeling, statistical business analysis, and anomaly detection in operational data. He emphasizes practical applications and bridges academia with industry through training and consultancy. The recent publications reflect a strong trend in statistical process control, AI-driven environmental modeling, and educational technology. His work combines theoretical rigor with real-world impact, particularly in quality control and data-driven decision-making. Scientific Awards and Recognitions: Hadiah Sanjungan from PERSAMA for PhD thesis Professional Technologist (MBOT) SAS Certified Professional: AI and Machine Learning Dr Lee actively supervises students and contributes to academic service as a peer reviewer for journals like IEEE Access and PLoS ONE. He is a chief investigator in an active research project focused on building data-driven organizations using AI and data warehousing. He also collaborates with SAS Institute Malaysia as an accredited trainer, delivering data science programs to industry and government. He is involved in professional communities and serves as a technical reviewer and judge for data science competitions and conferences, contributing to the advancement of the field in Southeast Asia.
Muhammad Fermi Pasha is a Senior Lecturer at the School of Information Technology, Monash University, Malaysia. He holds a PhD in Brain-inspired Computing from Universiti Sains Malaysia and has been actively contributing to research and teaching since joining Monash. His academic roles include lecturing and serving as Chief Examiner for courses such as FIT1008, FIT2085, FIT9123, FIT5152, and FIT3175, focusing on computer science, business information systems, and usability. PhD in Brain-inspired Computing, Universiti Sains Malaysia (2010) MSc in Computer Science, Universiti Sains Malaysia (2006) BCompSc (Hons) in Software Engineering, Universiti Sains Malaysia (2003) Dr. Pasha's research spans computational neuroimaging, intelligent network security, digital health, and big data analytics. His work integrates artificial intelligence with healthcare applications, including Alzheimer's diagnosis, mHealth platforms, and secure medical data systems. He emphasizes evolving systems, machine intelligence, and neocortex memory modeling. His projects often involve interdisciplinary collaboration and community engagement. The recent publications highlight a strong trend in AI-driven healthcare solutions, secure data systems, and behavioral analysis using deep learning. His work combines computer vision, natural language processing, and cybersecurity to address real-world challenges in medicine and public health. Themes such as microexpression recognition, EHR clustering, blockchain for IIoT, and flood modeling using CNNs reflect his diverse yet cohesive research vision. Awards for software solutions and research projects as team lead or member (specific names not provided) Dr. Pasha supervises multiple PhD students and leads significant research grants, including projects on microexpression recognition, Alzheimer's prediction, and flood modeling. He collaborates with national and international researchers and contributes to UN Sustainable Development Goals, particularly in health and education. His lab work involves developing intelligent systems for medical and environmental applications. He leads or participates in key research labs and teams focused on AI in healthcare, network security, and sustainable computing. These teams develop frameworks for early disease detection, secure data sharing, and environmental resilience using advanced machine learning and blockchain technologies.
Dr. Balamurugan Soundararaj is a Research Fellow at the City Futures Research Centre, University of New South Wales (UNSW), specializing in geospatial analysis, urban planning, and data visualization. With 8 years of experience in academia and industry, he contributes cutting-edge research to digital valuation models, predictive analytics, and spatio-temporal urban studies. His work integrates machine learning and geospatial technologies to address property market dynamics, transportation systems, and smart city challenges. Fields of Interest: Urban and Regional Planning Geospatial Data Modeling Information Visualization Spatial Statistics Human Geography Grants: 2021: API Australian Property Research and Education Fund Grant for 'Towards understanding regional property markets' 2021: ADA Faculty Research Partnership Scheme Grant for Steiner/Montessori school system research 2018: UCL Yusuf Ali Travel Grant for Complex Systems Conference presentation Scientific Recognition: 2019: Best Paper (Early Career Researcher), GIS Research UK 2015: Best Paper, Construction Track, RICS Annual Conference His research employs advanced analytical methods to visualize and model large-scale spatial data, focusing on transparent communication of complex urban systems. Key publications analyze bikeability improvements, property market tools, and footfall dynamics using Wi-Fi signals.
Boris Jean Eudes Beranger is a Lecturer in Data Science at the School of Mathematics and Statistics, UNSW Sydney, and an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). He holds a PhD in Statistics from Université Pierre and Marie Curie (Paris 6) and UNSW Sydney (2016), and an MSc in Mathematics from Université Pierre and Marie Curie (2011). His research focuses on Extreme Value Theory applied to environmental extremes (e.g., floods, heatwaves), Symbolic Data Analysis for complex datasets, and Statistical Theory for big data challenges. He has developed novel methods for analyzing histogram-valued data and max-stable processes in high dimensions, with applications in climate science and telecommunications. Key publications include work on tail density estimation using kernel methods, composite likelihood approaches for spatial extremes, and likelihood-based inference for aggregated data. His work emphasizes computational efficiency and robustness in extreme event modeling. Awards: J.B. Douglas Award (2014), Outreach Participation Award (2019) Grants: ACEMS Research Support Scheme (2018–2021), multiple industry collaborations Teaching: Courses include Data Science fundamentals, regression analysis, and extreme value theory Labs/Teams: UNSW Data Science Hub (uDASH), leading the UNSW Statistics Seminar Series
Dr. Imdad Ullah is a Lecturer at the School of Computer Science, The University of Sydney. He holds a PhD in Computer Science & Engineering from UNSW Sydney and has held research positions at Data61 CSIRO Australia, TU Darmstadt (Germany via Alexander von Humboldt Fellowship), and SLAC National Accelerator Laboratory. His expertise spans privacy-enhancing technologies, IoT security, blockchain, and machine learning applications in cybersecurity. **Education:** PhD in Computer Science & Engineering, UNSW Sydney (2012-2016) Bachelor/Master qualifications not explicitly listed **Research Focus:** Privacy-preserving systems for mobile advertising and IoT Blockchain frameworks for edge/fog computing environments Machine learning-driven intrusion detection systems Secure healthcare frameworks using federated learning His work emphasizes interdisciplinary collaboration, including global networks like TEIN and IEEE. **Awards:** Alexander von Humboldt Research Fellowship (2014) UNSW Tuition Fee Scholarship (2012-2016) **Teaching & Leadership:** Teaches courses in machine learning, cybersecurity, and IT strategy IEEE SPARK Coordinator (2024-2026) Member of curriculum design and exam evaluation committees **Funding:** Secured AUD millions in grants for projects like blockchain-IoT security frameworks and privacy-preserving mobile systems.
Dr. Stephen Tierney is a Senior Lecturer in Business Analytics at the Sydney Business School, University of Sydney . His research focuses on machine learning , computer vision , image processing , and recommendation systems , with contributions to subspace clustering, data visualization, and image restoration. He advises two current PhD students: Pengqing SHI (on scalable graph neural networks) and Widhiyo SUDIYONO (on stock market prediction via deep learning). Recent work includes policy-relevant studies on NSW teachers' pay trends and labor market skill dynamics. His publications span top-tier journals like Signal Processing and conferences such as IEEE CVPR, covering topics ranging from efficient subspace clustering algorithms to image fusion techniques in remote sensing. Key contributions include robust functional manifold clustering (2021) and low-rank sequential subspace clustering (2015). Interdisciplinary projects merge business analytics with technical domains like computer vision and signal processing.
Dr Angela Hecimovic is a Senior Lecturer at the University of Sydney's Sydney Business School, specializing in the Discipline of Accounting, Governance & Regulation. She holds a BEcon (Hons) from Sydney and a PhD from Macquarie University, focusing on 'Assurance of Natural Resource Management'. Her research bridges auditing, non-financial assurance, and technology adoption, particularly AI's impact on audit practices. She collaborates with audit practitioners, professional bodies like CAANZ, and regulators such as the AUASB. Teaching roles include courses like Contemporary Issues in Auditing and Work-Integrated Learning . She has received multiple teaching awards: Inaugural Dean's Award for Teaching Excellence (2019) Dean's Award for Teaching (2019–2021) Wayne Lonergan Teaching Award Research projects include grants on audit team dynamics, data analytics, and remote education strategies. Notable works examine AI adoption in audits, non-financial reporting in public sectors, and pandemic-era teaching innovations. She co-leads initiatives like the Re-energised Teaching project and collaborates internationally on audit standard-setting reforms. Labs/Teams: Active contributor to the University's Audit Research Group and professional partnerships with audit firms to develop teaching resources and audit quality frameworks.
Dr. Kane Koh is a Lecturer in the Department of Economics Finance & Marketing at RMIT University, Melbourne, Australia. He previously served as an Associate Lecturer at the University of Melbourne from July 2023 to February 2025. He holds a PhD from the University of Melbourne, specializing in marketing science methodologies, including analytics, machine learning, and econometric modeling. His research explores phenomena such as pet adoption, monetization strategies, live streaming services, and customer valuation, supported by over $26,000 in grants from the Marketing Science Institute. Education Completed PhD at The University of Melbourne, focusing on marketing science applied to structured and unstructured data. Research Interests Substantive: Digital Marketing, Marketing Technology, Customer Experience, Creator Economies, Content Monetization, Online Toxicity, Live Streaming, Charities, Crowdfunding, Sensory Experiences Methodological (Structured Data): Quantitative Marketing, Time Series Modelling (Panel Vector Autoregression) Methodological (Unstructured Data): Machine Learning (Video/Audio/Text), Text Analyses Awards Runner-up, AMS Review/Sheth Foundation - Doctoral Competition (2025) Winner, ANZMAC Conference Honorable Mention (2024) Winner, University of Melbourne Doctoral Best Paper Award (2024) Finalist, SMA Dissertation Competition (2024) Highly Recommended Runner-Up, ACSPRI Fellowship (2023) Winner, Melbourne Doctoral Poster Sessions (2023) Finalist, 3MT Competition (2023) Winner, RMSIG Doctoral Award (2023) Winner, AMS Review/DoCCA (2023) Teaching & Collaboration Coordinates MKTG1415 Digital Marketing at RMIT. Open to PhD supervision, industry partnerships, and data-driven collaborations. Expertise in leveraging analytics for digital marketing strategies.
Dr. Thanh Tam Nguyen is a Lecturer at the School of Information and Communication Technology, Griffith University, Gold Coast Campus. He holds a PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and is a Fellow of the Higher Education Academy (FHEA). His academic and research profile is centered on advancing Big Data and Smart technologies through efficient and trustworthy AI systems. His educational background includes a Doctor of Science and a Master of Science, both from EPFL. He is actively involved in high-impact research and has published over 65 papers in top-tier venues such as SIGMOD, VLDB, SIGIR, ICDE, IJCAI, VLDBJ, TKDE, and Pattern Recognition, with over 35 in CORE A* journals and conferences. His work has attracted more than 4,200 citations (h-index 35+). Dr. Nguyen's research focuses on Big Data Analytics, Social Network Mining, Stream Processing, Privacy-Preserving Machine Learning, Recommender Systems, Explainable AI, and Graph Neural Networks . He aims to bridge human insights with data models to ensure transparency and trust in data-driven decisions. His recent work explores misinformation management, machine unlearning, and federated learning, with applications in social networks, healthcare, and sustainable agriculture. The 15 most recent publications reflect a strong trend in privacy-preserving AI, explainability, federated learning, and graph-based modeling . Key themes include machine unlearning, adversarial robustness in recommender systems, heterogeneous graph representations, and trustworthy AI deployment. His work is frequently published in ACM and IEEE venues, indicating sustained excellence in computer science and AI research. Fellow of the Higher Education Academy (FHEA) Dr. Nguyen has secured over $1 million in research funding from government (DFAT, A4I, AKF), industry (CSIRO, Ubitech, Johnson & Johnson), and international bodies (NAFOSTED, ETRI, KARI). He serves as a guest editor for IEEE Journal of Biomedical and Health Informatics, area chair for ACL and EMNLP, and reviewer for top journals like TKDE, JVLDB, and CSUR. He supervises multiple PhD students and collaborates with leading international researchers such as Prof. Karl Aberer (EPFL), Prof. Björn Schuller (Imperial College), and Prof. Hongzhi Yin (UQ). He is a key contributor to the Responsible Big Data Lab at Griffith University and leads projects on misinformation management, AI safety, and sustainable agriculture through AI-powered traceability. His impact extends to policy and industry, particularly in cybersecurity and trustworthy AI adoption.
Dr. Michelle Dunn is a Senior Lecturer in the School of Engineering at Swinburne University of Technology, where she leads research in robotics and signal processing. She serves as the academic lead for the Swinburne Rover Team and the lead of Space Robotics within the Swinburne Space Technology and Industry Institute. Her interdisciplinary work bridges engineering, space technology, industrial monitoring, and cultural heritage conservation. School: School of Engineering University: Swinburne University of Technology Academic Rank: Senior Lecturer Dr. Dunn's research is centered on robotics and signal processing, with key interests in Space Robotics, Collaborative Robotics, Lunar Dust Mitigation, and Assistive Technologies. She applies signal analysis techniques to diverse fields including steelmaking, where she uses acoustic and vibration signals to monitor processes, and art conservation, where she integrates spectroscopy and imaging to analyze paintings. Her work combines experimental modeling, sensor development, and data analytics to solve real-world engineering challenges. Her recent publications span robotics in agriculture, lunar dust mitigation, additive manufacturing defect detection, and acoustic monitoring in steelmaking. These works reflect a strong trend toward applying robotics and advanced signal processing to practical, interdisciplinary problems. She frequently collaborates with industry and government agencies, securing funding from the ARC and Department of Industry, Science and Resources. Scientific awards include: Vice-Chancellor's Teaching Excellence (Higher Education) Award (2018) Vice-Chancellor's Teaching Excellence (Higher Education) Award (2013) Dr. Dunn actively supervises PhD and Master’s students across diverse projects, including heat flow monitoring, assistive mobility devices, UAV-based parking systems, and EEG-based voice detection. She has been principal investigator on multiple ARC-funded grants, such as the ARC Training Centre for Collaborative Robotics in Advanced Manufacturing and projects focused on sound and vibration monitoring in steelmaking. Her research contracts include development of portable parasite detection systems and cuffless blood pressure monitoring. She leads and contributes to laboratory-based research in the Swinburne Space Technology and Industry Institute, focusing on space robotics and lunar dust mitigation. Her team also operates experimental setups for acoustic and vibration monitoring in industrial models, supporting innovation in steelmaking and manufacturing.
Associate Professor Ivan Lee is affiliated with the University of South Australia's STEM division at Mawson Lakes Campus. He actively supervises research degrees and contributes to interdisciplinary research intersecting computer science, food safety, and smart materials. Research Interests: Specializes in machine learning applications for hyperspectral imaging in food contamination detection Develops novel graph learning algorithms for real-time driver fatigue monitoring Investigates eco-friendly synthesis techniques for graphene-based nanomaterials Advances intelligent sensing systems for structural health monitoring Publications Trends: Recent work focuses on spatio-temporal graph modeling, multi-objective optimization algorithms, and applying deep learning to food safety challenges like aflatoxin detection. His research spans computer vision, IoT systems, and sustainable materials. Supervision: Currently serves as a research degree supervisor at University of South Australia. No scientific awards mentioned in available records.
