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Georgy SofronovView profile

Associate Professor

• Statistical Analysis
• Operations Research
• Change-Point Detection
• +6 more

Georgy Sofronov is an Associate Professor in Statistics and Director of Research Training at Macquarie University's School of Mathematical and Physical Sciences. He holds a PhD in Probability Theory and Mathematical Statistics from Moscow State University (2002). Previously, he held academic positions at the University of Queensland and the University of Wollongong. He serves on editorial boards for Statistical Papers and Methodology and Computing in Applied Probability , and is Treasurer of the Modelling and Simulation Society of Australia and New Zealand. His research focuses on change-point detection, statistical quality control, optimal decision rules, and computational statistics. Research Interests : Change-point problem, statistical quality control Optimal decision rules, operations research Markov chain Monte Carlo methods, Cross-Entropy method Statistical modelling and data analysis Statistical Software (R-package 'breakpoint') Affiliations : Biomolecular Discovery Research Centre Centre for Risk Analytics Data Horizons Research Centre Smart Green Cities Research Centre Transforming Energy Markets Research Centre Teaching : Courses include Statistical Theory , Probability , Market Research and Forecasting , and Statistical Inference . Grants & Projects : SmartCrete CRC (2020–2027) Multiple change-point detection methods for big data (2017–present) Awards : Learning and Teaching Award (2016) Award for Excellence in HDR Supervision (2021) Collaborations : Active in Australia, New Zealand, and international networks focusing on computational statistics and risk analytics.

• Electrical and Electronic Engineering
• Applied Mathematics
• Physical Sciences
• +3 more

Ali Moradi Amani is a Senior Lecturer at the RMIT University School of Engineering , specializing in Electrical & Electronic Engineering. Based at the City Campus in Australia, his research focuses on clean energy systems, machine learning applications in power networks, and smart grid technologies . Affiliation: RMIT University, School of Engineering Academic Rank: Senior Lecturer Email: ali.moradiamani@rmit.edu.au His recent publications (2020-2025) demonstrate expertise in: AI-driven energy sustainability solutions Electric vehicle charging infrastructure optimization Community battery storage systems Multi-energy system demand response Dynamic grid operating envelopes Solar system demand management He actively supervises Masters and PhD research projects in these areas, emphasizing scalable and robust energy solutions through data-driven approaches and advanced optimization techniques.

Michael H. BreitnerView profile

Adjunct Professor

• Energy Systems
• Digital Transformation
• IT Security
• +7 more

Michael H. Breitner is an Adjunct Professor at the Institute of Business Information Systems (Wirtschaftsinformatik) within the Faculty of Economics and Management at Leibniz University Hannover. He serves on the Executive Board of the Leibniz Research Centre for Energy and holds leadership roles in the Finance Group and Faculty Council. His research focuses on energy systems transformation, digital transformation in universities, AI/ML applications, big data analytics, and cybersecurity. His academic career includes extensive work in energy informatics, mobility logistics, and decision support systems. Key projects include SiNED (secure power grids), TEN.efzn (digital energy research), and 5GAPS (5G-based positioning systems). He collaborates with industry partners like Lenze and government bodies such as the Lower Saxony Ministry of Science. Publications span journals like Decision Support Systems, Energy Policy, and Electronic Markets, with a focus on AI applications, energy systems optimization, and IT governance. His work addresses challenges in renewable energy deployment, smart city technologies, and ethical AI implementation across global contexts. Breitner advises on interdisciplinary projects involving energy policy, logistics optimization, and cybersecurity frameworks. His lab, BEAM (Behavioural Energy Analytics & Modelling), integrates behavioral science with energy system research.

• Traffic Conflict Analysis
• Connected and Automated Vehicles
• Human Factors and Driving Behaviour
• +5 more

Professor Md. Mazharul Haque is a distinguished academic and researcher in transportation engineering at Queensland University of Technology (QUT), Australia. He serves as Head of the School of Civil & Environmental Engineering and holds a Professorial rank. His expertise spans econometrics, artificial intelligence, and traffic safety. He has secured over $3 million in research funding, published over 110 peer-reviewed articles, and achieved an h-index of 27 (Scopus) and 33 (Google Scholar). His work is recognized through prestigious roles like Associate Editor of Accident Analysis & Prevention and ASCE Journal of Transportation Engineering . Research focuses include Traffic Conflict Analysis, Connected and Automated Vehicles (CAVs), Human Factors in Driving, and Black Spot Identification. He co-founded the startup Advanced Mobility Analytics Group (AMAG), developing AI-driven road safety tools. Awards include the TRB Outstanding Paper Award (2020, 2016) and the John Kirby Award (2014). He has advised multiple PhD students on topics like CAV integration, safety modeling, and traffic flow optimization. His grants address real-time risk assessment, safety evaluation of traffic signals, and CAV behavioral impacts. Education includes a PhD from the National University of Singapore (2005–2009) and degrees from Bangladesh University of Engineering and Technology (BUET). Professional memberships include TRB Committees, ASCE, and editorial roles in leading journals. Key contributions include pioneering AI-based video analytics for crash risk forecasting and advancing econometric models for transportation systems.

James McGreeView profile

Professor

• Statistical Methodology
• Bayesian Design
• Clinical Trial Design
• +1 more

Professor James McGree is Professor of Statistics in the School of Mathematical Sciences at Queensland University of Technology, holding a PhD from the University of Queensland. His research focuses on optimal experimental design, Bayesian adaptive methodology, and innovative clinical trial development. Research interests span: Bayesian adaptive designs for clinical and ecological applications Statistical methodology for complex spatial and temporal data Optimization of sampling strategies for environmental monitoring His publication portfolio demonstrates extensive methodological development in Bayesian statistics applied to environmental science, healthcare, and industrial optimization. Recent work includes flood mitigation strategies, coral reef monitoring, and clinical trial designs for rare diseases. Professor McGree actively supervises doctoral candidates working on Bayesian system identification, subsampling methods for big data, and robust clinical trial designs.

Ashish BhaskarView profile

Professor

• Transport Data Analytics
• Traffic Simulation
• Intelligent Transport Systems
• +2 more

Ashish Bhaskar is Professor of Civil Engineering (Transport) at Queensland University of Technology, where he co-leads the Business and Engineering Systems domain at the Centre for Data Science. His research develops data-driven solutions for traffic congestion through three interconnected themes: (1) Transport big data analytics using multi-source traffic data, (2) Network-scale modeling and simulation, and (3) Real-time traffic control algorithms. Current projects include: AI for traffic and transit congestion forecasting Data-driven traffic assignment using vehicle trajectories Evaluation of connected vehicle technologies Network-scale arterial traffic state prediction

Md Abul BasharView profile

Research Fellow

• Deep Learning
• Natural Language Processing
• Machine Learning Applications
• +2 more

Md Abul Bashar is a Research Fellow at the Queensland University of Technology's Centre for Data Science and School of Computer Science. His research focuses on developing deep learning and machine learning solutions for natural language processing tasks, including abusive content detection, misinformation analysis, and social media mining. Industrial applications include unconscious bias detection systems deployed at Fortune 500 companies and automated marketing strategy generation commercialized by Robotic Marketer. Research areas span multimodal fusion (uncertainty-guided meta-learning in 2025, pre-gating attention mechanisms in 2024), longitudinal data analysis (GAN-based imputation in 2024), and cybersecurity applications (Log4Shell threat detection in 2023). Articles demonstrate strong emphasis on NLP techniques adapted for low-resource scenarios through transfer learning and generative models. Significant Applications Unconscious bias detection for enterprise content management Automated marketing report generation (commercialized) Misogyny detection featured in Forbes, Daily Mail Energy payment propensity prediction Indigenous heritage repatriation support systems

Yanming FengView profile

Professor

• Global Navigation Satellite Systems (GNSS)
• Geodetic Data Analytics
• Internet of Things (IoT)
• +5 more

Professor Yanming Feng is a Professor in the School of Computer Science at Queensland University of Technology (QUT), Australia. He holds a PhD in satellite geodesy and spatial science from Wuhan Technical University of Surveying and Mapping (now Wuhan University). His research focuses on data science, GNSS algorithms, geodetic data analytics, IoT, vehicular networks, and machine learning applications. He has led major projects under ARC grants and CRC programs, including roles in the Cooperative Research Centre for Satellite Systems (CRCSS) and Automotive CRC. Education: PhD, MSc, BSc (Wuhan University) Teaching: Networks security, wireless mobile networks, spacecraft dynamics Key Research: GNSS positioning, space debris monitoring, IoT systems for environmental monitoring Leadership: Editor-in-Chief of Journal of Global Positioning Systems (2009-2013), Editorial Board member of 'Engineering' (Chinese Academy of Engineering) Industry Collaboration: Partnerships in spatial information, intelligent transport, and manufacturing sectors Research interests include millimetre-precision positioning, geodetic data analytics via machine learning, and vehicular safety applications. He has supervised ~24 HDR students and leads teams in projects like real-time satellite monitoring of river flows and IoT-based landslide detection. Major articles span GNSS time series modeling (2022), machine learning for orbit prediction (2020), and vehicular network synchronization (2018). His work bridges academic innovation with industrial needs in precision positioning and smart systems. Current Supervision: Projects include GNSS-based soil settlement analysis and satellite network controllers Completed Supervisions: Over 20 PhD topics in GNSS ambiguity resolution, vehicular safety, and deformation monitoring Labs/Teams: Active in QUT's Centre for Data Science and CRC initiatives for satellite systems and spatial information.

Aiden PriceView profile

Researcher

• Environmental Health
• Spatial Analysis
• Population Health
• +4 more

Dr. Aiden Price is a Researcher at Queensland University of Technology (QUT), affiliated with the Faculty of Science and the School of Mathematical Sciences. He serves as a Research Associate and Project Manager for the AusEnHealth Project, focusing on environmental health strategic planning via digital twin technology. His expertise spans spatial and temporal analyses of health and environmental data, including bushfire impacts and Antarctic conservation through aesthetics. Education: B.Math (Honours Class I, 2015) and PhD (2019, QUT) in error control theory, with a focus on LDPC codes under Prof. Harry Bartlett. He has held roles as a Research Engineer (AOS Group), Statistics Consultant (DNRME Queensland), and Lecturer at QUT in statistics and machine learning. Research Interests: Spatial epidemiology, Bayesian statistics, environmental health modeling, and applications of machine learning in public health. His work addresses issues like modifiable areal unit problems in disease mapping and spatial heterogeneity in health outcomes. Publications: Over 15 peer-reviewed articles in journals such as PLoS ONE, Scientific Reports, and the International Statistical Review. Key topics include spatial vulnerability indices, breast cancer disparities, and crowdsourcing ecological data. Grants & Advising: Supervised Wala Areed’s thesis on spatio-temporal health modeling. Engaged in interdisciplinary projects like the AusEnHealth digital twin for national health planning. Active in the ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS). Labs/Teams: Centre for Data Science, AusEnHealth Project team, and collaborations with organizations like DNRME and AOS Group.

• Applied Statistics
• Computational Statistics
• Statistical Theory
• +11 more

Dr. Ian Hunt is an Adjunct Senior Lecturer in Agriculture and Food Systems at the University of Tasmania, affiliated with the Tasmanian Institute of Agriculture (TIA). He leads statistics and data science services for researchers and PhD students, and teaches statistics and programming (R, Python, MATLAB) at undergraduate and postgraduate levels. His academic work spans multiple disciplines, integrating statistical theory with practical applications in agriculture, medicine, law, and environmental science. His research interests include applied statistics, computational statistics, statistical inference, model selection, and uncertainty quantification. He specializes in translating complex statistical concepts for non-specialists and applying them to real-world scientific challenges. His expertise supports interdisciplinary research in food safety, plant science, animal health, and climate-smart agriculture. His recent publications span diverse fields such as agricultural science, food microbiology, medical research, and statistical methodology. These works demonstrate a strong trend in using data science and statistical modeling to optimize agricultural outputs, improve food safety, and inform clinical and legal decisions. Common themes include predictive modeling, experimental design, and the use of novel sensor technologies (e.g., eNose) for quality assessment. Chartered Statistician (CStat) Fellow of the Royal Statistical Society (RSS) Member of RSS Committee for Statistics and the Law Dr. Hunt is actively involved in research supervision and grant-funded projects. He supervises PhD students working on eNose data analysis, smart-farm dairy systems, and cannabinoid production. His current grants include projects on virtual fencing, greenhouse gas reduction in livestock, integrated pest management, and soil health monitoring. These projects are funded by organizations such as the Department of Natural Resources and Environment Tasmania, Horticulture Innovation Australia, and the CRC for High Performance Soils. He contributes to national and international research communities through invited talks at Royal Statistical Society conferences and co-chairing seminars on law and algorithms. His work bridges data science with practical agricultural and societal challenges, emphasizing collaboration, innovation, and knowledge transfer.

Joanne EnticottView profile

Associate Professor

• Clinical Biostatistics
• Epidemiology
• Health Services Research
• +4 more

Associate Professor Joanne Enticott is a leading biostatistician at Monash University's Monash Centre for Health Research & Implementation (MCHRI). She specializes in big data analytics , learning health systems , and women's health , with a focus on translating research into policy and equitable healthcare practices. She co-developed the Monash Learning Health System framework, now implemented across $16M+ initiatives, and led the landmark TOBOGM trial published in the New England Journal of Medicine (2023). Education : PhD (University of Melbourne, 2012), MSc (Research), BSc (Mathematics). Former roles include Senior Research Fellow positions at the University of Sydney and Royal District Nursing Service. Research Interests : Leveraging big data to improve healthcare outcomes, maternal health, mental health, and vulnerable populations. She leads the MCHRI Biostatistical Consultancy Service and contributes to UN Sustainable Development Goals related to health equity. Awards : 2013 Advancing Women's Research Success Award, 2024 ScholarGPS Top 0.5% Scholar Ranking, and over AU$39M in research funding (including $25M in last 5 years). Advising & Grants : Supervises 9 PhD students (5 primary), completed 8, and leads the Digital Data-Driven Healthcare Industry Partnership mentoring 11 candidates. Active in grant leadership across MRFF and NHMRC programs. Labs/Teams : Leads the Big Data and Learning Health Systems research stream at MCHRI, part of the Executive Team overseeing >$50M in current grants. Collaborates internationally on projects like dementia prevention and maternal health interventions.

Song CheenView profile

Research Fellow

• Health Informatics
• Bioinformatics
• Machine Learning
• +3 more

Dr. Song Cheen is a Research Fellow at the Microbiome Research Centre, School of Pharmacy, Monash University Malaysia. His work focuses on health informatics, bioinformatics, and machine learning applications in healthcare. He earned a PhD in Bioinformatics (2021–2024) and a BSc (Hons) in Bioinformatics (2015–2018), both from University of Malaya. Education: PhD in Bioinformatics, University of Malaya (2021–2024) BSc (Hons) in Bioinformatics, University of Malaya (2015–2018) Research interests include the human microbiome’s role in health/disease, predictive modeling, and AI-driven healthcare solutions. Current projects investigate microbiome responses to probiotics, alcohol effects on gut microbiota, and FOS supplementation in elderly populations. Projects (2025–2026): Protein/Probiotics supplementation’s impact on faecal microbial composition Alcohol’s effects on gut microbiota and probiotic protection FOS supplementation for underprivileged elderly gut health Advising/Grants: Collaborator on multiple grants focused on microbiome research and healthcare analytics. No formal student advisees listed. Labs/Teams: Active contributor to the Microbiome Research Centre. Work aligns with UN SDGs related to health and innovation.

• Cyber Security
• Big Data Analytics
• Software Engineering
• +2 more

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 WangView profile

Research Fellow

• Privacy-Enhancing Technologies
• Secure AI Systems
• AI-Driven Business Intelligence
• +5 more

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 GaoView profile

Research Fellow

• Urban Climate Mitigation
• Climate Change Adaptation
• Thermal Comfort and Energy Efficiency
• +3 more

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.