Find Academic Supervisors

• Financial Analysis
• Equity Valuation
• Life Insurance
• +3 more

Demetris Christodoulou is an Associate Professor in Accounting, Governance and Regulation at the University of Sydney. He holds a BEcon from Piraeus University, an MSc(Fin) from the University of York (UK), and a PhD from Athens University of Economics and Business (AUEB). His research focuses on applying data analytics, econometrics, and visualization techniques to financial analysis, equity valuation, life insurance, and financial advice. He co-directs the PEMA research group, specializing in productivity and performance measurement analytics, and previously led the MEAFA research group (2007–2022). He has collaborated extensively with industry partners including Deloitte and Australian insurers, and developed training programs for over 1,000 executives. His work includes open-source contributions to Stata software and the Graph Workflow platform, alongside $662k in workshop-generated funds supporting academic programs. He has advised multiple PhD students and taught at leading universities globally. Education: BEcon in Economics (Econometrics), Piraeus University MSc in Finance, University of York (UK) PhD in Accounting and Financial Analysis, Athens University of Economics and Business His research interests span financial reporting models, life insurance underwriting strategies, and behavioral finance. Recent projects address dishonesty mitigation in insurance disclosures and the adviser effect on customer disclosures. He has published widely in top journals like the Review of Accounting Studies and Stata Journal , and his work was featured in The Australian for insights on insurance fraud reduction. He maintains international collaborations, including visiting roles at Columbia Business School and the University of Cyprus, and serves on advisory boards for organizations like Behaviour.ai. Publications highlight methodological innovations in econometrics and visualization, with 2025's upcoming Stata Journal paper advancing time-series analysis techniques. His grants include partnerships with industry on longitudinal studies of insured lives, aiming to improve risk modeling and public policy insights.

Dylan CampbellView profile

Lecturer

• Computer Vision
• Optimization
• Machine Learning
• +3 more

Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.

• Bayesian methodology
• Variable selection and model averaging
• Nonparametric regression models
• +3 more

Scientia Professor Robert Kohn is a distinguished academic at the University of New South Wales, holding a position in the School of Economics within the UNSW Business School. With a career spanning several decades, Professor Kohn has established himself as a leading expert in statistical methodology and econometric modeling. His research has significantly contributed to Bayesian statistics and computational methods for complex data analysis. Professor Kohn's research focuses on advanced statistical methodologies including Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modeling, multivariate Gaussian and non-Gaussian regression, and Markov chain Monte Carlo simulation algorithms. His work bridges theoretical statistics with practical applications across economics, finance, and cognitive science. His research demonstrates a consistent trajectory toward developing more efficient computational methods for complex statistical models, with recent work emphasizing variational Bayesian methods, particle filtering techniques, and applications to time series analysis. Analysis of his recent publications (2022-2025) reveals a strong focus on advancing computational statistical methods, particularly in Bayesian inference for complex models. His work shows increasing integration of machine learning techniques with traditional statistical methods, especially in handling high-dimensional data and complex time series structures. Professor Kohn has made significant contributions to variational inference methods, particle-based computational techniques, and applications to financial time series and cognitive modeling. Professor Kohn has maintained an exceptionally productive research career with continuous publication output since the 1970s, demonstrating remarkable longevity and adaptability in his research focus as statistical methodologies have evolved. His work shows strong international collaboration, particularly with researchers in Australia, the United States, and Europe, reflecting his standing in the global statistical community.

• Robust Statistics
• Dimension Reduction
• Meta-Analysis
• +3 more

Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.

Tao ZouView profile

Associate Professor

• Covariance regression modeling
• Network data modeling
• Financial statistics
• +3 more

Tao Zou is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans covariance regression modeling, network data analysis, and applications in financial and environmental statistics. He earned a Ph.D. in Statistics in 2016. Ph.D. in Statistics, 2016 Dr. Zou’s work pioneers covariance regression, where covariances are modeled as functions of covariiates. Key contributions include robust estimation techniques, spatio-temporal modeling, missing data imputation via semi-supervised learning, and distributed data aggregation. His methods address challenges in high-dimensional and non-Euclidean data analysis. Recent publications (2025–2023) explore quasi-score matching for spatial autoregressive models, regularization in network regression, functional principal component analysis for complex data, and environmental applications like PM2.5 pollution studies. These works emphasize robustness, scalability, and interdisciplinary relevance in economics, finance, and environmental science. Dr. Zou collaborates on projects like the 2023 Data Analysis App to Empower Assessment of Immunogenicity of Biologics (Co-Investigator). While his student supervision list isn’t explicitly provided, his methodological advancements influence big data and spatial statistics. He contributes to open-access software and continues expanding covariance regression for non-normal and functional data.

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.

Scott Anthony SissonView profile

Professor

• Computational Statistics
• Bayesian Statistics
• Statistical Extreme Value Theory
• +3 more

Professor Scott Anthony Sisson is a leading academic at the University of New South Wales (UNSW) , holding the position of Professor of Statistics and Data Science in the School of Mathematics and Statistics . He serves as Director of the UNSW Data Science Hub (uDASH) and Deputy Director of the UNSW AI Institute (UNSW.ai) . Previously, he was Deputy Director of the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and held leadership roles in the Australasian Society of Bayesian Analysis and Statistical Society of Australia . PhD in Statistics (Bristol University, 2002) MSc in Environmental Statistics and Systems (Lancaster University, 1997) BSc in Mathematics and Statistics (Lancaster University, 1996) His research focuses on computational statistics and Bayesian inference , with expertise in machine learning , extreme value theory , and high-dimensional data analysis . He develops simulation-based algorithms for complex statistical problems and applies these to diverse scientific challenges like seagrass decline, urban flood modeling, and drug delivery systems. His recent work spans quantum computing for statistics, graphon modeling, and synthetic likelihood methods. Scientific awards include: 2024 Fellow of the International Society of Bayesian Analysis 2023 Fellow of the Institute of Mathematical Statistics 2017 ARC Future Fellowship 2010 Queen Elizabeth II Research Fellowship 2006 John Yu Fellowship His advising team has mentored students in statistical modeling, Bayesian computation, and applied data science. Grants from the Australian Research Council and industry collaborations support his research in government and scientific applications. He contributes as Associate Editor for Journal of Computational and Graphical Statistics and Statistics and Computing .

• Pulsar Astrophysics
• Radio Astronomy
• Gravitational Wave Astronomy
• +3 more

Dr. Jim Palfreyman is a Research Fellow in the School of Natural Sciences at the University of Tasmania, specializing in pulsar astrophysics with a particular focus on the Vela pulsar and its glitch phenomena. Based at the Physics department and working from the Mount Pleasant Observatory, he has established himself as a leading researcher in observational pulsar astronomy through his unconventional academic journey. Dr. Palfreyman's educational background reflects his non-traditional path into academia: PhD (2018): "A Long-term Single-pulse Study of the Vela Pulsar" - University of Tasmania MSc (2012): "Consecutive Bright Pulses in the Vela pulsar" - University of Tasmania BSc(Hons) (1985): "Graphics Simulation of Solar Eclipses" - University of Tasmania After completing his undergraduate studies, Dr. Palfreyman worked in computing for decades before returning to fulfill his passion for astronomy. His research centers on the detailed observation and analysis of pulsar behavior, with special emphasis on the Vela pulsar's glitch phenomena where it suddenly speeds up in rotation. His most significant achievement came in 2016 when he captured the Vela pulsar glitch using the University of Tasmania radio telescope at Mount Pleasant - the first time individual pulses had been observed during a glitch, a feat that had eluded astronomers worldwide for 50 years. His findings were published in Nature, establishing him as a significant contributor to the field. Analysis of Dr. Palfreyman's publication record reveals a clear trajectory from pure pulsar observation toward the intersection of pulsar astronomy and gravitational wave physics. His most recent work (2022-2025) demonstrates increasing collaboration with major international gravitational wave projects like LIGO-Virgo-KAGRA, applying sophisticated statistical methods to search for gravitational wave signatures from known pulsars. This evolution reflects the growing importance of multi-messenger astronomy in understanding neutron star physics. While no specific formal awards are documented in the available information, Dr. Palfreyman's work has received significant recognition through: Publication in Nature for capturing the Vela pulsar glitch of 2016 High citation counts for his key papers (e.g., 139 citations for his Nature paper) Regular invitations for media commentary on astronomical phenomena Dr. Palfreyman actively supervises research students and has developed what he describes as "the largest single-pulse pulsar archive in the world" through continuous monitoring at the Mount Pleasant Observatory. He collaborates extensively with international teams, including the LIGO gravitational wave observatory and NICER (an observatory on the International Space Station). His research program receives funding through multiple projects examining pulsar behavior and its implications for fundamental physics, particularly the connection between pulsar glitches and gravitational wave emission. Based at the Mount Pleasant Observatory, Dr. Palfreyman leads a research team dedicated to continuous monitoring of pulsars, with particular focus on the Vela pulsar. His facility provides critical long-term data for international collaborations studying neutron star physics. The observatory's monitoring program has established a unique dataset that continues to yield important insights into pulsar behavior, especially regarding glitch phenomena and their potential connection to gravitational wave emission.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

• Sample Survey Design and Analysis
• Robust Statistical Methods
• Graphical Methods in Statistics
• +4 more

Professor Raymond Chambers is a distinguished statistician currently serving as an Honorary Fellow at the School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong (2022–present). His career spans leadership roles at the University of Southampton (2003–2006 as Director, Southampton Statistical Sciences Research Institute; 1995–2000 as Professor and Head of Department of Social Statistics; 1999–2000 as Leverhulme Professor) and the Australian National University (1989–1995 as Senior Lecturer in Statistics). He completed his PhD in Biostatistics at Johns Hopkins University (1979–1983) and has supervised 9 PhD students in areas like Small Area Estimation , Robust Inference , and Survey Methodology . His research focuses on Sample Survey Design and Analysis , Robust Statistical Methods , Statistical Modelling and Inference , and Analysis of Computer-Linked Data . Recent publications emphasize Small Area Estimation , Probabilistic Data Linkage , and Non-Linear Spatial Models , reflecting his expertise in integrating survey data with advanced statistical techniques. Professor Chambers has received multiple accolades, including Elected Member of the International Statistical Institute Elected Fellow of the American Statistical Association Elected Fellow of the Academy of Social Sciences in Australia Leadership roles as President of the International Association of Survey Statisticians (2011–2013) He has secured significant funding from Australian and international agencies, including Discovery Projects and Linkage International grants, supporting research in Small Group Analysis , Missing Data Handling , and Longitudinal Survey Methodology . His professional contributions include editorial roles at the Journal of the Royal Statistical Society and The Annals of Statistics .

Simon TurnerView profile

Research Fellow

• Biostatistics
• Interrupted Time Series Designs
• Meta-Analysis
• +3 more

Simon Turner is a Research Fellow at Monash University’s School of Public Health and Preventive Medicine, affiliated with the Methods in Evidence Synthesis Unit. He holds a PhD (2021) from Monash University, focusing on statistical methods for interrupted time series analyses. His expertise includes biostatistics, meta-analysis, and evidence synthesis, particularly applied to public health interventions. He contributes to the Cochrane Collaboration as a biostatistician. Educational Background PhD in Biostatistics, Monash University (2017–2021) Masters of Biostatistics (Star Graduate), University of Melbourne (2015) Masters of Science (Astronomy), Swinburne University of Technology (2008) Bachelor of Teaching (Honours), University of Melbourne (1999) Bachelor of Science (Honours), Monash University (1997) Research Interests Simon’s work centers on improving statistical methodologies for interrupted time series (ITS) analyses and meta-analyses. He develops tools like the Banksia plot for visualizing ITS data and collaborates on search filters to identify ITS studies in literature. His research aligns with UN Sustainable Development Goals related to public health and health equity. Awards & Contributions Star Graduate Award (Masters of Biostatistics, University of Melbourne) Leading author on methods for ITS analysis, including datasets and graphical techniques Labs & Collaborations Active in the Methods in Evidence Synthesis Unit and collaborates internationally on public health research. His work emphasizes methodological rigor in evaluating intervention impacts.

• Data Mining
• Database Systems
• Machine Learning
• +7 more

Jixue Liu is an Associate Professor at the School of Information Technology and Mathematical Sciences, University of South Australia, with a research focus spanning data mining, database systems, machine learning, and privacy-preserving technologies. His work bridges theoretical advancements with practical applications in remote sensing, social media analysis, and ethical AI. Education: PhD in Computer Science (Databases), 2001, University of South Australia Master by Research in Mining Engineering, 1987, Beijing University of Science and Technology Bachelor's in Mining Engineering, 1982, Xian University of Architecture and Technology Research interests center on causal inference (e.g., causal decision trees, front-door criterion), privacy-preserving data publishing (e.g., k-anonymity, composition attack mitigation), and machine learning for social media (e.g., adverse drug reaction detection, protest event prediction). Recent work explores algorithmic fairness and XML schema transformations. Article trends reveal expertise in causal modeling (2022-2024), with methodologies like variational autoencoders and instrumental variables, alongside privacy-preserving analytics and event detection systems . His interdisciplinary approach integrates computer science, statistics, and domain-specific applications in forestry and healthcare. Scientific awards include the Best Paper Award at ADC 2014. Major grants from ARC and CRC programs (2005-2023) highlight sustained funding for projects on entity resolution, causal rule mining, and fair predictive modeling. He supervises research degrees and teaches database management, XML databases, and data mining.

• Numerical and computational mathematics
• Data management and data science
• Artificial intelligence
• +2 more

Musa Mammadov is a Senior Lecturer in Data Science at Deakin University's School of Information Technology, part of the Faculty of Science Engineering and Built Environment. His research focuses on data science, machine learning, and computational mathematics with applications in environmental modeling, healthcare analytics, and financial systems. Education: Doctor of Philosophy from University of Ballarat Research Interests: Specializing in numerical and computational mathematics, Mammadov develops advanced machine learning techniques for complex classification problems while exploring optimization methods in mathematical economics. His work spans environmental modeling applications in Sri Lanka's Kalu River Basin, healthcare fraud detection algorithms, and financial market analysis. Scientific Contributions: The recent publications highlight his work in hydrological forecasting using deep learning architectures, anomaly detection in medical billing systems, and probabilistic modeling of financial indices. His methodological contributions include improving Bayesian network classifiers and developing novel dependency estimation techniques. Academic Roles: Mammadov serves as editorial board member for Optimization Letters and Annals of Data Science . He supervises doctoral students in data science projects including health provider billing analysis and satellite downlink scheduling optimization.

Dianne CookView profile

Professor

• Data Science
• Data Visualization
• Exploratory Data Analysis
• +5 more

Prof. Dianne Cook is a Professor of Business Analytics at Monash University's Department of Econometrics and Business Statistics, affiliated with Impact Labs. A Fellow of the American Statistical Association and Editor of the Journal of Computational and Graphical Statistics, her research focuses on data visualization, exploratory data analysis, and statistical graphics. Her work bridges statistical inference and visualization, leveraging crowd-sourcing and eye-tracking to validate visual discoveries. Major contributions include software tools like ggobi and cranvas, and methodologies like the 'tour' for high-dimensional data exploration. Current projects include spatio-temporal bushfire visualization and Melbourne pedestrian data analysis. Key collaborations span physics data visualization and multidimensional analysis. Awards include ASA Fellowship. Projects include Accessibility Enhancements for the R Journal and Multivariate Spatio-Temporal Visualization for bushfire risk. Teaching and advising emphasize open-source software and reproducible research. She leads RiskLab projects on econometrics and explores applications in climate change, education, and health. Her work contributes to UN SDGs like Sustainable Cities (Goal 11) and Quality Education (Goal 4).