Find Academic Supervisors

• Econometric Forecasting
• Applied Macroeconometrics
• Time Series Analysis
• +12 more

Dr. Jing Tian is a Senior Lecturer in Economics (Finance) at the Tasmanian School of Business and Economics, University of Tasmania, where she contributes significantly to research, teaching, and academic leadership. She is also an associated researcher at the Centre for Applied Macroeconomic Analysis. Her academic foundation includes a PhD in Economics from the Australian National University, complemented by earlier degrees from the same institution and Zhongnan University of Economics and Law. PhD in Economics, Australian National University, 2010 Master of Economics, Australian National University, 2005 Graduate Diploma in Economics with Distinction, Australian National University, 2004 Bachelor of Economics, Zhongnan University of Economics and Law, 2001 Her research centers on econometric forecasting and applied macroeconometrics, with a focus on time series modeling, multi-horizon forecasts, and trend-cycle-seasonal decomposition. She applies these methods to diverse areas such as weather, energy, agriculture, tourism, and macroeconomic policy. Her work emphasizes enhancing forecasting accuracy and utility for real-world decision-making, particularly under uncertainty. The most recent publications reflect a strong trend toward interdisciplinary research, integrating economic modeling with environmental and climate challenges—especially in Antarctica and marine ecosystems. Articles span topics from electricity demand forecasting to the valuation of ecosystem services, demonstrating a growing emphasis on sustainability and policy relevance. Dr. Tian has received multiple research grants from organizations including the Australian Institute of Marine Science, Minerals Council of Australia, and the Department of Agriculture, Water and the Environment. These projects cover biodiversity valuation, drought resilience, mining productivity, and agricultural innovation, showcasing her ability to secure competitive funding and lead impactful research. Valuing Biodiversity Improvements in the Great Barrier Reef (2024–2026) Measuring Australia's Mining Productivity (2023–2024) Drought Resilience Tasmania (2021–2026) Integrating Smallholder Households into Beef Supply Chains in Vietnam (2022–2026) She is actively engaged in supervision, currently mentoring one PhD and two honors students, and has successfully guided seven doctoral candidates to completion. Her teaching includes core units such as Data Analysis for Business, Econometrics (Honours), and Forecasting for Economic Decision-Making. Leadership roles include serving as Chair of the Tasmanian School of Business and Economics from February 2025 and previously as a leader within the College of Business and Economics from 2020 to 2023.

Ole ManeesoonthornView profile

Associate Professor

• Bayesian Inference
• Time Series Forecasting
• Volatility Modelling
• +1 more

Ole Maneesoonthorn is an Associate Professor in the Department of Econometrics & Business Statistics at Monash University. He specializes in Bayesian inference, time series forecasting, volatility modeling, and financial econometrics. Currently accepting PhD students since 2012, his research contributes to UN Sustainable Development Goals through methodological advancements in statistical and financial modeling. He leads two major research projects: Property Price Index Development (University of Melbourne collaboration, 2022-2023) Loss-based Bayesian Prediction (2020-2025, collaborating with G. Martin, D. Frazier, and R. Hyndman) His work emphasizes practical Bayesian methods for economic and financial applications, with recent focus on copula models, hybrid variational inference, and optimal forecasting frameworks. Outputs include peer-reviewed articles in top journals like Journal of Business and Economic Statistics and International Journal of Forecasting .

Pavel KrivitskyView profile

Researcher

• Social Networks
• Computational Statistics
• Infectious Disease Epidemiology
• +7 more

Dr. Pavel Krivitsky is a researcher at the School of Mathematics and Statistics of the University of New South Wales (UNSW) Sydney . His work focuses on developing methods and models for analyzing complex network data, with applications in epidemiology, social sciences, and national security. He maintains popular R packages like ergm and contributes to the Statnet Project and uDASH (UNSW Data Science Hub). PhD in Statistics, University of Washington (2009) MS in Statistics, University of Washington (2006) BS in Biometry and Statistics, Cornell University (2003) His research spans social network analysis , computational statistics , and stochastic modeling , particularly for unusual or indirectly observed network data. Recent publications highlight advancements in multi-criteria recommender systems, network sampling techniques, and corrections for international datasets. Scientific Awards : 2024 Mid-career Impact Award for Fundamental Research (UNSW Science) 2023 Journal of the American Statistical Association Discussion Paper 2019 Freeman Award for social structure research 2019 Richards Award for open-source software development Dr. Krivitsky supervises PhD students Saman Forouzandeh (2021–2025) and Yun Pan (2022–). He teaches courses like MATH3831 (Statistics in Social and Market Research) and DATA5002 (Data Visualisation), previously instructing MATH5855 (Multivariate Analysis) and DATA1001 (Fundamentals of Data Science).

• Machine Learning
• Gaussian Processes
• Robotics
• +7 more

Dr. Arman Melkumyan serves as a Senior Research Fellow at the University of Sydney, specifically affiliated with the Australian Centre for Field Robotics and the Rio Tinto Centre for Mine Automation within the Faculty of Engineering. His research focuses on applying advanced machine learning techniques to solve complex problems in mining automation and geoscience. He maintains an active research profile with numerous publications in top-tier journals and conferences, demonstrating his expertise in bridging theoretical machine learning with practical mining applications. Dr. Melkumyan's research interests center on the application of machine learning, particularly Gaussian processes, to mining automation challenges. His work spans hyperspectral imaging analysis, geological boundary detection, ore/waste classification, and measure-while-drilling data interpretation. He has developed innovative approaches for probabilistic geological modeling, spatial data analysis, and autonomous systems in mining environments. His research demonstrates strong interdisciplinary connections between computer science, geostatistics, and mining engineering, with a particular emphasis on creating practical solutions for the mining industry. Analysis of Dr. Melkumyan's recent publications reveals a consistent focus on applying Gaussian processes and other machine learning techniques to mining automation problems. His work shows a progression from theoretical developments in covariance functions and regression methods toward increasingly practical applications in real-world mining operations. Key trends include the integration of multiple data sources, development of robust algorithms for noisy field data, and creation of automated systems for geological interpretation. His research has strong industry relevance, particularly for iron ore mining operations in Australia. Dr. Melkumyan has been granted multiple patents related to mining automation technologies, including methods for boundary detection in natural gamma logs, ore tracking systems, and resource characterization techniques. These patents demonstrate the practical impact and commercial potential of his research. As a Senior Research Fellow, Dr. Melkumyan contributes significantly to collaborative research projects at the intersection of robotics, machine learning, and mining engineering. His work involves close collaboration with industry partners through the Rio Tinto Centre for Mine Automation, ensuring that his research addresses real-world challenges in the mining sector. He has contributed to numerous projects focused on autonomous systems for geological modeling, ore classification, and resource estimation. Dr. Melkumyan is an active member of the Australian Centre for Field Robotics, a world-leading research group specializing in robotics applications for field environments including mining. His work within the Rio Tinto Centre for Mine Automation places him at the forefront of research into autonomous mining systems, where he focuses on the data analysis and machine learning components that enable intelligent decision-making in automated mining operations.

• Statistics Education
• Data Science Education
• Interdisciplinary Data Projects
• +3 more

Jaslene Huan Lin is a Senior Lecturer (Educational Focussed) at the School of Mathematics and Statistics , University of Sydney. She coordinates large first-year statistics units, focusing on enhancing student motivation and persistence during critical transition periods. Her role includes curriculum development, educational innovation, and training casual teaching staff through her position as Teaching Coordinator. Research interests center on statistics education in Higher Education, with emphasis on first-year student transitions and pedagogical strategies. She actively contributes to interdisciplinary data projects and provides statistical consultancy for collaborative research initiatives, aligning with the Faculty of Science's research strength in Data and Decisions. Awards and honors include: Advance HEA Fellowship (2024) NCMIC-JMPT Paper Award Second Prize (2023) for bullying and adolescent health research Her publications span statistics education, symbolic data analysis, and ecological meta-analysis, demonstrating expertise in educational frameworks, data science methodologies, and cross-disciplinary applications.

• Igneous Petrology
• Metamorphic Petrology
• Geochemistry
• +3 more

Dr. Maya Kamenetsky is a Research Fellow at the University of Tasmania's School of Natural Sciences, specifically within the Earth Science Discipline at CODES ARC (Centre for Ore Deposit and Earth Sciences, Australian Research Council). She has been with the university since 2005 and works as a Casual Staff Member, indicating a part-time academic appointment. Her educational background includes a PhD in Geology from the University of Tasmania (2002-2005) and earlier studies at Moscow State University, from which she graduated in 1984. She is fluent in Russian, which facilitates her international collaborations, particularly with Russian researchers. Dr. Kamenetsky's research focuses on the study of phase evolution and compositional changes of fluids recorded in melt/fluid inclusions from quartz and other minerals. Her expertise spans melt inclusion techniques, high-temperature experiments, and analytical methods for data processing. She applies this knowledge to interpret petrological and geochemical issues related to magma genesis, fractionation, and evolution across different geodynamic settings. Her work has significant implications for understanding ore-forming processes, particularly at the Olympic Dam deposit in Australia. Analysis of Dr. Kamenetsky's recent publications reveals a strong focus on melt and fluid inclusion studies across various geological settings. Her research spans kimberlite systems, Olympic Dam-style deposits, and Mesoproterozoic magmatism. A recurring theme is the application of melt inclusion techniques to understand magma evolution, fluid processes, and ore formation. Her work bridges fundamental petrological research with practical applications in mineral exploration. Supervised two PhD students to completion: Adam Blake Abersteiner (2016-2020) on kimberlites and diamonds, and Naomi Potter (2015-2019) on carbonatites Currently leads the Olympic Dam fluid/melt inclusion project (2021-2024), funded by BHP Billiton Ltd with $610,714 Active researcher with publications through 2024 demonstrating ongoing research productivity Dr. Kamenetsky maintains extensive international collaborations, as evidenced by her co-authorship on papers with scientists from Australia, Russia, Germany, and other countries. Her work on kimberlite systems, Olympic Dam deposit, and Mesoproterozoic magmatism has contributed significantly to understanding the evolution of magmatic systems and ore-forming processes.

Rand LowView profile

Associate Professor

• Portfolio Optimization
• Risk Management
• Quantitative Finance
• +5 more

Rand Low serves as Honorary Associate Professor at the University of Queensland's School of Business and Associate Professor of Quantitative Finance at Bond Business School. His expertise bridges academia and industry with extensive experience leading quantitative research teams at Bank of America Merrill Lynch and BlackRock in New York City. He actively contributes to financial discourse through media engagements with major outlets including Australian Financial Review, BBC, and ABC News, while advising governmental bodies on bond market development. Dr Low holds a Doctor of Philosophy from the University of Queensland. His educational background includes Chartered Engineer designation from Engineers Australia earned during engineering management roles at Honeywell on landmark projects including GOMA and Mater Mothers' Hospital. Professor Low's research spans four critical domains in quantitative finance: Portfolio Optimization: Specializing in multi-asset strategies (commodities, fixed income, private equity) using vine copulas for crisis-period dependence modeling Risk Management: Developing frameworks for credit/market/operational risk including VaR/CVaR metrics and regulatory model validation Digital Assets: Pioneering research on cryptocurrency derivatives, blockchain applications, and ESG integration in digital finance Systematic Trading: Creating factor-based and event-driven strategies validated through neural network analysis of commodity risk premia His 15 most recent publications reveal three dominant trends: increasing integration of machine learning in commodity risk analysis (2023-2025), growing emphasis on ESG factors in credit rating determinants (2024), and innovative applications of blockchain technology beyond finance into conservation (2025). The research consistently addresses volatility spillovers during crises while expanding into digital asset derivatives and sustainable investment frameworks. Professor Low's scientific recognition includes: GSITA Award and 3MT Competition Winner during PhD studies Dean's Award for Research Higher Degree Excellence Australia Awards - Endeavour Fellowship Australian Institute of Business and Economics (AIBE) Scholar designation Research Fellowship on Portfolio Optimization & Risk Management Techniques He actively supervises doctoral candidates in corporate credit risk modeling, robo-advisor development, and multi-asset optimization. His funded research includes the Accounting and Finance Association of Australia and New Zealand grant (2014-2015) on risk-adjusted retirement metrics and the UQ Postdoctoral Research Fellowship (2014-2016) on crisis-period portfolio optimization. Industry collaborations include advisory roles for BitOrb (Digital Assets Exchange) and Australian Bond Exchange. Professor Low leads cross-sector initiatives connecting academic research with financial practice, having directed quantitative teams at top-tier institutions where he defended model development practices to US regulators including the Federal Reserve. His current focus involves applying statistical learning to automate investment processes across cryptocurrency, commodities, and corporate credit markets.

Peter BakerView profile

Associate Professor

• Biostatistics
• Agricultural Statistics
• Public Health
• +5 more

Dr. Peter Baker is an Honorary Associate Professor at the Queensland Alliance for Agriculture and Food Innovation , with a 40-year career in biostatistics. He has held roles at the School of Public Health (2009–2020) and CSIRO (20 years). His work bridges agricultural research, public health, and medical statistics, with a focus on reproducible research via R, Make, and Git. 40+ years of statistical consulting Developed Bayesian methods for sugarcane breeding Contributed to alcohol policy and mosquito net efficacy studies Research Interests span biostatistical methods for epidemiology, agricultural data analysis, and reproducible workflows. He specializes in: Statistical computing (R, Make, Git) Propensity score analysis for observational studies Bayesian modeling in medical contexts Crop modeling with weather data Birthweight and maternal environmental factors Development of bespoke statistical software Supervision includes advisory roles for PhD projects on crop modeling, epidemiological methods, and medical statistics. Current collaborations involve Professor Graeme Hammer and Professor Peter Sly .

Andrew WoodView profile

Professor

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +5 more

Professor Andrew Wood is a faculty member at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans non-Euclidean statistics, theoretical statistics, computational methods, and applied statistics in sciences and medicine. Research Interests : Non-Euclidean statistics, directional statistics, statistical shape analysis, asymptotic theory, computational statistics, stochastic differential equations, and applications in science/medicine. Grants : Funded by Engineering and Physical Sciences Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), and Australian Research Council Discovery Projects. Editorial Roles : Former Joint Editor of Journal of the Royal Statistical Society, Series B and current Associate Editor of Biometrika . Research Trends : Recent publications focus on robust statistical methods for non-Euclidean data, computational approaches for complex distributions, principal component analysis for high-dimensional datasets, and geometric inference on manifolds. Applications include microbiome analysis, surface fractal dimension estimation, and spherical regression models. Supervision & Collaboration : Registered as a supervisor at ANU, with collaborations across disciplines including molecular biology, environmental science, and computational mathematics.

Mareike DresslerView profile

Lecturer

• Real and Computational Algebraic Geometry
• Polynomial and Convex Optimization
• Matrix and Tensor Computation
• +3 more

Dr. Mareike Dressler is a Senior Lecturer (Assistant Professor, tenure-track) at the School of Mathematics and Statistics, UNSW Sydney. She holds a PhD in Mathematics from Goethe University Frankfurt (2018) and has held postdoctoral positions at Max Planck Institute for Mathematics in the Sciences (Leipzig) and University of California, San Diego. Education: PhD (Mathematics, 2018), M.Sc. (2013), B.Sc. (2010) from Goethe University Frankfurt Prior Affiliations: Postdoctoral researcher at MPI MiS (Leipzig), Stefan E. Warschawski Assistant Professor at UCSD, Semester Postdoctoral Fellow at Brown University (ICERM) Her research focuses on Real and Computational Algebraic Geometry, Polynomial and Convex Optimization, Matrix and Tensor Computation, and their applications in Data Science and Machine Learning. She has developed optimization techniques using sums of nonnegative circuit polynomials and explored connections between convex geometry and algebraic structures. Her recent publications span topics including multivariate Chebyshev polynomials, matrix completion ranks, SONC cone duality, and applications of semidefinite programming. Collaborations include work with Venkat Chandrasekaran, Bernd Sturmfels, and Roland Lasserre, with methodological contributions to polynomial optimization and tensor analysis. At UNSW Sydney, she teaches undergraduate and postgraduate courses including MATH 1131 (Mathematics 1A, Algebra) and MATH 5185 (Special Topic: Nonnegativity and Polynomial Optimization). Her research group engages with both theoretical and applied problems in optimization and algebraic methods.

Gordana PopovicView profile

Senior Lecturer

• Statistics
• Statistical Ecology
• Biostatistics
• +3 more

Dr. Gordana Popovic is a Senior Lecturer at the University of New South Wales (UNSW), affiliated with the Statistical Consulting Unit within the Research & Enterprise division. She specializes in statistical ecology, biostatistics, and computational modeling, providing methodological guidance and collaborative support to applied researchers. Her work spans ecological data analysis, clinical studies, and environmental monitoring. Her research focuses on developing and applying advanced statistical techniques, including copula models, spatial analysis, and longitudinal studies. Recent projects address wetland vegetation dynamics, cancer prognosis biomarkers, and antibody response modeling. She also teaches short courses in statistics using R and SPSS. Dr. Popovic has supervised research students such as Ben Maslen and contributed to interdisciplinary collaborations in fields like oncology, immunology, and conservation biology. Her publications emphasize methodological innovation and empirical applications in ecology and clinical research. Key journals where her work appears include Cancer , Methods in Ecology and Evolution , and PLOS Medicine . Her methodological expertise includes maximum likelihood estimation, graphical models, and design optimization for multivariate data collection.

Tom StindlView profile

Lecturer

• Statistics
• Computational Statistics
• Self-Exciting Point Processes
• +6 more

Dr. Tom Stindl is a Lecturer at the School of Mathematics & Statistics , UNSW Sydney. His research focuses on statistical inference for self-exciting point processes, particularly the renewal Hawkes process and its marked/multivariate variants, with applications in finance, seismology, crime, and bushfire modeling. Core research areas: Hawkes Processes, Computational Statistics, and Spatiotemporal Modeling. Supervised PhD/MRes/Honours students including Jason Lambe, Zhe Han, and Toby Mufford. Developed R packages RHawkes and MRHawkes for statistical modeling. His recent publications (2017–2025) analyze Hawkes processes for earthquake declustering, financial risk forecasting, and Bayesian ETAS modeling. He received the Early Career Teaching Excellence award from UNSW's Faculty of Science for courses like MATH3821 and DATA3001 . A current Australian Research Council Discovery Project (2024–2026, $463,452) explores Hawkes processes with challenging data.

• Optimization
• Numerical Analysis
• Applied Mathematics
• +4 more

Chayne Planiden is a Senior Lecturer in the School of Mathematics and Applied Statistics at the University of Wollongong, where he has been appointed since 2022. His research focuses on mathematical optimization, particularly nonsmooth optimization techniques including regularization methods, derivative-free algorithms, and the VU-algorithm. He specializes in proximal mappings, simplex gradient approximations, and Hessian approximation methods. Dr. Planiden's research spans both theoretical and applied mathematics, with significant applications in energy market modeling. His work addresses renewable energy integration, microgrid management, and electricity market design using optimization frameworks. Key methodologies include derivative-free optimization techniques and numerical analysis approaches for complex systems. In terms of research supervision, Dr. Planiden currently mentors three PhD students working on projects involving optimization algorithms, reaction-diffusion equations, and financial option pricing. He has successfully supervised one PhD candidate to completion on decarbonized electric grid frameworks. No scientific awards or honors are mentioned in the available information.

Luke MazurView profile

Research Fellow

• Applied Statistics
• Computational Statistics
• Statistical and Quantitative Genetics
• +3 more

Dr. Luke Mazur is an active Research Fellow at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. His work focuses on computational methods for statistical modeling in agricultural and financial contexts. Education: Bachelor of Mathematics and Economics (First Class Honours, 2014), PhD in progress Key Affiliation: Centre for Bioinformatics and Biostatistics at University of Wollongong (CBaDSSPI) Research Interests: Mazur specializes in optimizing linear mixed models for datasets from sustainable primary industries. His methodology emphasizes: Exploitation of sparse matrix techniques Multi-threaded computational approaches Integration of efficient third-party libraries Modern coding practices for statistical analysis Publishing Trends: His research bridges computational statistics and practical applications, with recent work covering financial network analysis and data privacy techniques. Earlier publications focus on statistical formulations for agricultural trials and multivariate data analysis. Laboratory Affiliation: Currently works with the Computational Biology and Data Science Solutions for Primary Industries (CBaDSSPI) research team.

• Statistical Inference
• Bootstrap Methods
• Data Privacy Protection
• +4 more

Dr. Pauline Ding is a Lecturer at the School of Mathematics and Applied Statistics , University of Wollongong, since 2022. She holds a Ph.D. in Mathematical Statistics from the Australian National University and previously worked as a consulting statistician at the same institution. Research Interests: Statistical inference in linear mixed models, bootstrap methods, multivariate density estimation, and data privacy. Teaching: Focuses on statistical disclosure control, missing data, complex models, and bootstrap applications. Funding: Involved in grants for high-performance computing upgrades and AI ethics research. Publications: Span healthcare AI, postoperative delirium, data privacy, and socioeconomic disparities in health outcomes. Her work emphasizes methodological innovation and interdisciplinary applications. Supervision: Actively supervises PhD projects on synthetic longitudinal data and cardiovascular machine learning, with completed work on diabetes risk and data privacy.