Find Academic Supervisors

• Water quality monitoring and modeling
• Data-driven prediction modeling
• Bayesian Network modeling
• +5 more

Dr. Edoardo Bertone is a Senior Lecturer at Griffith University's School of Engineering and Built Environment - Architecture and Design. He holds a PhD in Water Resources Engineering from Griffith University and Bachelor/Master degrees in Civil Engineering from the Polytechnic University of Turin. His research focuses on data-driven modeling, Bayesian Networks, and System Dynamics applied to water resources management, climate change adaptation, and the water-energy nexus. He is affiliated with Griffith's Cities Research Institute and Australian Rivers Institute, collaborating on projects with water utilities, governments, and private entities. Dr. Bertone has received awards such as the 2024 PVC Science Excellence in Teaching and the JSPS Fellowship (2022). He supervises doctoral and master's students in areas like water quality management and climate change impacts. Education: PhD in Engineering (Griffith University, 2015); MEng and BEng in Civil Engineering (Polytechnic University of Turin, 2009-2011). Research Interests: Water quality modeling, drinking water optimization, data-driven prediction, climate change adaptation, and sustainable development goals. He leads over 25 funded research projects, including initiatives on reservoir water quality management in Thailand, real-time nutrient monitoring, and cyanobacteria bloom modeling. Dr. Bertone’s work integrates advanced sensors, machine learning, and Bayesian networks to address environmental challenges. Awards: Listing includes PVC Excellence Awards (2024, 2017), JSPS Fellowship, and recognition as a Rising Star in Queensland Science (2015). Grants & Supervision: Principal supervisor for 10+ doctoral candidates and collaborator on projects funded by Seqwater, CSIRO, and the Ian Potter Foundation. Key grants include $745k for biofertilizer combatting eutrophication and $269k for coagulation optimization models. Dr. Bertone’s contributions extend to urban sustainability, co-editing the book *SeaCities: Urban Tactics for Sea-Level Rise* and developing frameworks for integrating SDGs into architectural education.

• Spatio-temporal Statistics
• Computational Statistics
• Environmental Informatics
• +3 more

Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.

• Hypersonics
• Fluid Dynamics
• Thermodynamics
• +4 more

Ingo Jahn is a Professor at The University of Queensland's School of Engineering. His academic career spans roles including R&D at Rolls-Royce (2007–2012) and academic positions at The University of Queensland (2012–2022). He holds an MEng (Oxford, 2005) and PhD (Oxford, 2011). Education: MEng in Engineering, University of Oxford (2005) PhD in Aerospace Engineering, University of Oxford (2011) Research Interests: Hypersonics: vehicle design, glide trajectory optimization, and aerothermodynamics Fluid Dynamics: computational methods, turbulence, and flow control Control Systems: model predictive control and co-design frameworks Thermodynamics: heat transfer in supercritical CO2 cycles and thermal protection systems His work bridges theoretical and experimental approaches, with a focus on hypersonic vehicle integration and propulsion systems. Publications: Recent articles emphasize hypersonic vehicle co-design, fluid-structure interaction, and experimental methods. Key themes include trajectory optimization, thermal management, and advanced simulation techniques. Grants & Awards: No awards explicitly listed, but extensive industry collaboration (e.g., Rolls-Royce) and leadership in high-impact projects indicate significant recognition. Supervision: Currently supervising 8 doctoral students on topics like hypersonic co-design, unstart prevention in ramjets, and scramjet trajectory optimization. Affiliations: Institute for Advanced Engineering and Space Sciences, AIAA, ASME. Active in conferences like AIAA SciTech and Global Power and Propulsion Society events.

Ruben Loaiza MayaView profile

Associate Professor

• Copula Modelling
• Bayesian Estimation Methods
• Time Series Analysis
• +4 more

Ruben Loaiza-Maya is an Associate Professor (Research) in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Econometrics from the University of Melbourne and an undergraduate degree in Economics from Universidad Nacional de Colombia (Medellin). His research focuses on Copula Modelling, Bayesian Estimation Methods, Time Series Analysis, and Macroeconomic/Financial Forecasting. Key contributions include advancements in variational inference, state space models, and robust forecasting techniques under model misspecification. He leads the active project 'Variational Inference for Intractable and Misspecified State Space Models' (2023–2026), funded as a Primary Chief Investigator. His work contributes to UN Sustainable Development Goals through methodological advancements in economic and financial analysis. Recent research emphasizes scalable Bayesian methods, hybrid variational approaches, and efficient computational techniques for high-dimensional models. Publications span prestigious journals like the International Journal of Forecasting, Journal of Econometrics, and Journal of Business and Economic Statistics. Notable collaborations include studies on copula-based time series forecasting and robust approximate Bayesian computation. His work bridges theoretical econometrics with practical applications in risk management and macroeconomic policy.

Xiaotian ZhengView profile

Assistant Professor

• Spatial Statistics
• Spatio-Temporal Statistics
• Point Process Modeling
• +5 more

Xiaotian Zheng is an Assistant Professor of Statistics at the University of Georgia. Previously, they were a Postdoctoral Research Fellow with the Australian Research Council Special Research Initiative Securing Antarctica's Environmental Future at the University of Wollongong, working under Professor Noel Cressie and Associate Professor Andrew Zammit-Mangion. They earned their Ph.D. in Statistical Science from the University of California, Santa Cruz, advised by Professors Athanasios Kottas and Bruno Sansó. Their research focuses on developing statistical and machine learning methods for analyzing complex, dependent data, particularly in ecological and environmental contexts. Key areas include spatial/spatio-temporal statistics, probabilistic downscaling, data integration, transfer learning, and statistical deep learning. Xiaotian's publications reflect their work on mixture transition distribution models, nearest-neighbor mixture models, and geostatistical frameworks for discrete-valued processes. These contributions emphasize Bayesian inference, computational efficiency, and real-world applications in environmental science and biodiversity modeling.

• Applied Mathematics
• Mathematical Modelling
• Computational Modelling
• +7 more

Professor Matthew Simpson is a leading figure in applied mathematics at the School of Mathematical Sciences, Faculty of Science, Queensland University of Technology (QUT). He holds the position of Professor of Applied Mathematics and is an Australian Research Council (ARC) Future Fellow, reflecting his sustained research excellence. His work bridges mathematical theory and biological applications, particularly in cell migration, tissue invasion, and multiscale modeling. BE (Environmental) Honours 1, University of Newcastle (1995–1998) PhD (with Distinction), Environmental Engineering, University of Western Australia (2000–2003) Research Fellow, Department of Mathematics and Statistics, University of Melbourne (2003–2006) ARC Postdoctoral Fellow, University of Melbourne (2006–2009) Lecturer (2010–2011) and Senior Lecturer (2011–2013), QUT Associate Professor (2013–2014), QUT Professor and ARC Future Fellow (2014–present), QUT Matthew Simpson’s research focuses on mathematical and computational modeling of biological systems , particularly collective cell motion, diffusion processes, and reaction-diffusion dynamics. His interests span multiscale modeling , random walk processes , cell biology , and numerical and computational mathematics . He develops and analyzes models to understand phenomena such as wound healing, cancer progression, and tissue engineering. His recent publications (2023–2025) demonstrate a strong trend toward integrating data-driven modeling , likelihood-based inference , and equation learning with traditional mechanistic models. These works emphasize parameter identifiability , uncertainty quantification , and prediction robustness in biological contexts. Themes include sharp-fronted wave propagation, mechanical cell interactions, tumor spheroid formation, and generalized diffusivity in food drying, showcasing the breadth and depth of his modeling expertise. Among his key accolades are: J.H. Michell Medal (2012) – Awarded by ANZIAM for distinguished research by an early-career applied mathematician in Australia and New Zealand. ARC Future Fellowship (2013–2017) – For the project 'New data-driven mathematical models of collective cell motion' (FT130100148). Professor Simpson has also played significant editorial and leadership roles, including: Executive Associate Editor, Journal of Engineering Mathematics Academic Editor, PLoS ONE Editorial Board Member, ANZIAM Journal Co-chair of the 2015 ANZIAM meeting He has supervised PhD students on topics such as moving boundary problems, first-passage times, stochastic simulations, and curvature-dependent growth in biological systems. His research projects have been funded by competitive Australian grants (ARC DP and FT schemes), including studies on 3D cell migration, ghrelin’s role in cell invasion, and epithelial-to-mesenchymal transition in cancer and wound healing. He is actively involved in developing computational tools for biological modeling and promoting best practices in scientific publishing.

Howard BondellView profile

Professor

• Statistical Data Science
• Model Selection
• Robust Estimation
• +5 more

Howard Bondell is a Professor of Statistical Data Science at the School of Mathematics and Statistics, University of Melbourne, since 2018. He serves as Head of School since 2021, Co-Director of the Melbourne Centre for Data Science, and holds an ARC Future Fellowship (2020-2024). Ph.D. in Statistics, Rutgers University (2005) Academic Career: North Carolina State University (2005-2018) His research focuses on model selection , robust estimation , regularisation , Bayesian methods , and uncertainty quantification in statistical and machine learning. His publications emphasize applications in regression analysis, quantile modeling, variable selection for high-dimensional data, and genetic data analysis. Scientific awards include: Fellow of the American Statistical Association (2017) ARC Future Fellow (2020-2024)

Dinh PhungView profile

Professor

• Machine Learning
• Deep Learning
• Generative AI
• +8 more

Professor Dinh Phung is the Head of the Department of Data Science & AI at Monash University. His research focuses on machine learning, deep learning, generative AI, and robust AI systems. He has authored over 250 publications, with applications in NLP, computer vision, digital health, and cybersecurity. Phung holds a PhD and BSc(Hons) in Computer Science from Curtin University. He leads major projects like 'Can Machines Unlearn?' and 'Trustworthy Generative AI', funded by the Australian Research Council and the Department of Defence. Education: Doctor of Philosophy, Computer Science, Curtin University (2005) Bachelor of Science (Honours), Computer Science, Curtin University (2001) Research Interests: Machine learning, deep learning, and generative models Optimal transport and Bayesian methods Robust and trustworthy AI Applications in digital health, cybersecurity, and autism research Key Projects (2023–2029): Can Machines Unlearn? (2025–2029): Safety in AI Trustworthy Generative AI (2024–2026): Foundation models Robust Machine Learning via Optimal Transport (2023–2025) Awards and Grants: Australian Research Council grants for AI safety and robustness Department of Defence funding for robust learning systems Collaborations: Global partnerships in AI ethics, cybersecurity, and healthcare. Active advisory roles, including with the Victorian Parliamentary Library.

• Statistics
• Machine Learning
• Psychology of Music
• +3 more

Dr. Linh Nghiem is a Lecturer in Statistics at the School of Mathematics & Statistics, University of Sydney. She specializes in both methodological and applied statistical research, focusing on measurement error modeling, dimension reduction, and graphical models. Her applied work involves collaborations with scientists exploring human perception of music and the societal impact of music on social empathy. Her research interests include longitudinal data analysis, privacy in data science, and experimental psychology of music at behavioral and neural levels. She is affiliated with the Sydney Southeast Asia Centre and actively contributes to interdisciplinary projects. Dr. Nghiem has secured grants such as the 2023 'Methodologies for complex datasets' under the Faculty Startup Scheme. She collaborates with institutions globally and maintains an active presence in academic communities through her ORCID profile and personal website.

Xuhui FanView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Bayesian Methods
• +5 more

Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.

• Biostatistics
• Clinical Trials
• Public Health
• +8 more

Professor Ian Marschner is a leading academic in biostatistics, currently holding the position of Professor of Biostatistics and Co-Director of Biostatistics at the NHMRC Clinical Trials Centre, University of Sydney. He has extensive experience spanning over 30 years, including roles as Professor and Head of the Department of Statistics at Macquarie University, Director of Biometrics at Pfizer, and Associate Professor at Harvard University. His research focuses on biostatistical applications in clinical trials, epidemiology, and public health, with a particular emphasis on adaptive trial designs, meta-analysis, and disease surveillance. Professor Marschner has contributed to major clinical trials in cardiovascular medicine, oncology, HIV/AIDS, neonatal/perinatal care, and COVID-19. He co-authored the book Inference Principles for Biostatisticians and is involved with the Biostatistics Collaboration of Australia (BCA) in developing and teaching the Masters of Biostatistics program. His grants include the NHMRC Centre of Research Excellence (AusTriM) and a National Critical Research Infrastructure Initiative grant totaling over $20 million. Research students under his supervision include Aydin HIBBERT, focusing on generalized joint regression models for longitudinal data. His work addresses methodological challenges such as bias in early-stopped trials, surrogate endpoints, and statistical frameworks for adaptive experiments. Recent contributions include risk modeling for diabetes, cardiovascular mortality prediction, and biomarker analysis in cancer therapies.

• Structural Health Monitoring
• Steel-Concrete Composite Structures
• Physics-Informed Machine Learning
• +6 more

Dr. Xinqun Zhu is an Associate Professor at the University of Technology Sydney (UTS) in the School of Civil and Environmental Engineering . He has held academic positions at Western Sydney University (2016-2017), University of Western Australia (2005-2009), and University of Manchester (2001-2005). His research spans structural health monitoring, steel-concrete composite structures, physics-informed machine learning, and advanced sensor systems.

• IoT Wireless Sensing and Networking
• 5G/6G Cellular Systems
• TeraHertz Communications
• +8 more

Associate Professor Zihuai Lin leads the IoT in Healthcare and Radar Imaging group at the University of Sydney's School of Electrical and Computer Engineering. He holds a PhD from Chalmers University of Technology and has prior experience at Ericsson Research and Aalborg University. His research focuses on IoT, 5G/6G systems, healthcare AI, TeraHertz communications, radar imaging, and wireless signal processing. Education: PhD in Electrical Engineering, Chalmers University of Technology, Sweden (2006) Postdoctoral work at Ericsson Research, Sweden Associate Professor, Aalborg University, Denmark (pre-Sydney role) Research Interests: IoT wireless sensing and healthcare applications 6G/THz communications and radar imaging Artificial Intelligence in signal analysis and network optimization MIMO/OFDMA systems and resource allocation Current Projects: 6G/THz communications and holographic MIMO Edge AI for healthcare IoT (eGate system) Ultra-low latency techniques for short-packet 5G Millimeter-wave power transfer and safety protocols Awards: 2021 IoT Awards Health Category Finalist (eGate system) Nominated for 2022 iTnews Best Health Project Advising & Labs: Supervising 8 PhD students in AI-driven healthcare, federated learning, and quantum imaging Led 10+ completed PhD projects in 5G/6G and wireless systems Affiliated with the Center of IoT and Telecommunication (CIoTT) and Sydney Nano Institute

• Bayesian statistics
• Computational statistics
• Environmental statistics
• +4 more

Roles & Affiliations: Distinguished Research Professor in Statistical Science at Queensland University of Technology (QUT), Director of QUT Centre for Data Science, and Associate Member of University of Oxford's Department of Statistics. Served as Deputy Director of ARC Centre of Excellence in Mathematical and Statistical Frontiers (2015–2021) and ARC Laureate Fellow (2015–2021). Education: BA (Hons) and PhD in Mathematical Statistics from University of New England, Australia. Completed post-doctoral roles at multiple Australian universities. Research Interests: Specializes in Bayesian statistical modelling, computational methods, and their applications in environmental science, genetics, healthcare, and industry. Leads projects on coral reef recovery, cancer epidemiology (Australian Cancer Atlas), and virtual citizen science platforms like Virtual Reef Diver. Her work emphasizes interdisciplinary collaboration, integrating complex data sources with advanced statistical techniques to address real-world challenges. Publications & Grants: Over 350 refereed journal publications and attracted >30 major grants. Recent focus areas include influenza epidemiology, spatial health disparities, and AI-driven early warning systems for climate-sensitive diseases. Active in developing methodologies for spatial statistics, small-area estimation, and federated learning. Awards & Recognition: 2024 Ruby Payne-Scott Medal (Australian Academy of Science), Pitman Medal (2016), first female recipient of this award in 35 years. Elected Fellow of Australian Academy of Science (2018), Academy of Social Sciences (2018), and Queensland Academy of Arts and Sciences (2018). Holds international roles including Vice-President of International Statistical Institute (2021–2025) and Scientific Council Member at Centre International de Rencontres Mathématiques (France). Supervision & Leadership: Supervised over 36 PhD students and leads teams in >50 collaborative projects. Current supervision includes 5 PhD and 4 Masters students at QUT. Founded the QUT Centre for Data Science and previously led the Collaborative Centre for Data Analysis, Modelling and Computation. Labs & Initiatives: Core contributor to the Australian Cancer Atlas 2.0, Virtual Reef Diver project, and Queensland's Learning Potential Fund. Active in global initiatives like the World of Statistics campaign and UN Big Data Task Teams.

David FrazierView profile

Professor

• Simulation-based inference
• Financial Econometrics
• Nonparametric and semiparametric econometric modeling
• +3 more

David Frazier is a Professor in the Department of Econometrics & Business Statistics at Monash University, specializing in simulation-based inference, financial econometrics, and nonparametric/semiparametric modeling. He teaches ETC 1010: Data Modeling and Computing. His research focuses on robust statistical methods, Bayesian computation, and model misspecification. Key projects include 'Consequences of Model Misspecification in Approximate Bayesian Computation' (2020-2025) and 'Loss-based Bayesian Prediction' (2020-2025). Recent work addresses forecasting in misspecified models, weak identification in econometric frameworks, and robust variational Bayes techniques. His contributions align with UN Sustainable Development Goals related to economic and environmental sustainability. Projects: 4 active/funded projects with ARC, Brown University, and international collaborators. Publications: Over 37 peer-reviewed articles in journals like the Journal of the American Statistical Association and Journal of Econometrics. Research interests include advancing Bayesian methodologies for complex models, with applications in asset pricing and economic forecasting. His work emphasizes reliability in statistical inference under model uncertainty and computational efficiency.