Andrey Vasnev is Professor of Business Analytics at the University of Sydney Business School. He holds an MA in Economics from NES Moscow, a PhD in Economics from Tilburg University, and an MEd from the University of Sydney. His research specializes in forecast combination methodologies for business, finance, and economics, particularly focusing on information integration from different temporal levels and optimal weighting strategies. His work addresses practical challenges in consolidating diverse predictions to improve forecast accuracy. Recent publications demonstrate his focus on advancing forecasting techniques through statistical innovations and machine learning applications in graph neural networks. His research consistently addresses both theoretical foundations and practical implementation challenges in predictive modeling.
Professor Mariano Kulish is a Professor of Macroeconomics at the School of Economics, University of Sydney. He holds a PhD in Economics from Boston College (2005) and previously worked at the Reserve Bank of Australia's Economic Research Department. His research focuses on macroeconomics, monetary policy, structural changes, and applied econometrics, with notable contributions to DSGE modeling, zero interest rate policies, and commodity price impacts. Key research themes include analyzing economies undergoing structural shifts, fiscal policy in open economies, and the implications of unconventional monetary policies like yield curve control. His work frequently addresses policy-relevant issues such as disinflation strategies, terms of trade volatility, and the stability of inflation-unemployment relationships. He has secured grants including the Australian Research Council's 2019 Discovery Project on fiscal policy in open economies. His publications span top journals like the Journal of Monetary Economics , Journal of Applied Econometrics , and European Economic Review . Recent work explores fiscal arithmetic in growth slowdowns, international spillovers of monetary policy, and the Dutch Disease hypothesis in commodity-rich economies. Professor Kulish’s research combines theoretical modeling with empirical analysis, emphasizing policy relevance. He maintains an active presence in academic collaborations and policy discussions, reflecting his dual role as a researcher and former central bank economist.
Thang Bui is a Lecturer (equivalent to tenure-track Assistant Professor) in Machine Learning at the School of Computing, Australian National University (ANU) since July 2022. Previously, he was a Lecturer at the University of Sydney (2018–2022) and spent two years (2019–2020) at Uber AI. He holds a PhD from the Cambridge Machine Learning Group at the University of Cambridge, supervised by Richard Turner and advised by Carl Rasmussen. Research Interests: His work focuses on probabilistic modeling and inference, Monte Carlo methods, distributed/continual learning, and model-based reinforcement learning. Current projects include uncertainty estimation in Gaussian processes and neural networks, adaptive models for changing environments, and interpretable machine learning techniques. Awards: Best Paper Award, ACL Workshop on Information Extraction from Scientific Publications (2023) Best Paper Award, NeurIPS workshop on Deep Learning through Information Geometry (2020) Best Paper Award, NIPS Workshop on Advances in Approximate Bayesian Inference (2015) Advising & Grants: Actively recruiting PhD/MPhil students and postdoctoral fellows. His research has been supported by grants focusing on Bayesian methods and scalable machine learning. He has advised students on topics ranging from Gaussian processes to federated learning and continual learning. Labs/Teams: Leads a research group exploring uncertainty quantification, adaptive learning systems, and probabilistic AI at ANU's College of Engineering and Computer Science.
Dr. Luca Maestrini is a Lecturer of Statistics at the Research School of Finance, Actuarial Studies & Statistics at the Australian National University (ANU), Canberra. His research focuses on methodological and computational statistics, with expertise in variational approximations, generalized linear mixed models (GLMMs), and statistical theory. He previously held postdoctoral positions at ANU (2022–2024) under Prof. Francis Hui and Prof. Alan Welsh, and at the University of Technology Sydney (2018–2022) under Prof. Matt Wand. He completed his PhD in Statistics at the University of Padova, Italy (2015–2019), studying under Prof. Nicola Sartori and co-supervisors Prof. Alessandra Salvan and Prof. Matt Wand. He also served as a Research Fellow at the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research interests span computational statistics, Bayesian inference, and applications in forensic science, ecology, and finance. Notable collaborations include work on inverse problems, multivariate abundance data analysis, and stochastic variational inference for GARCH models. He currently chairs the Australian Capital Territory branch of the Statistical Society of Australia. Key research trends in his articles include advancements in variational inference frameworks, asymptotic improvements to GLMMs, and statistical methods for postmortem interval estimation using lipid degradation biomarkers. His work bridges theoretical developments with practical applications in diverse fields. He has advised students through ANU’s research programs and contributed to ACEMS initiatives. His lab focuses on computational methods and applied statistical modeling, emphasizing interdisciplinary collaboration.
Lachlan Astfalck is a Research Fellow at the University of Western Australia's School of Physics, Mathematics and Computing, working on the TIDE ARC ITRH project. He specializes in statistical methodologies for oceanography, hydrodynamics, and paleoclimate modeling. His research focuses on spectral analysis, uncertainty quantification, and developing statistical methods for environmental applications. He holds a PhD (2019) and Bachelor of Engineering in Mechanical Engineering (2014) from the University of Western Australia. Dr. Astfalck's publications demonstrate a strong focus on developing novel statistical approaches for environmental systems. His recent work spans spectral analysis techniques, ocean turbulence modeling, ice sheet reconstruction, and Bayesian methods for climate applications. He actively develops open-source statistical tools and maintains several GitHub repositories related to spectral estimation methods. He is accepting PhD students in statistics and oceanography and has capacity for research collaborations.
Tom Lymburn is a Research Fellow at the School of Social Sciences, The University of Western Australia, affiliated with the Planning and Transport Research Centre. He holds a PhD in Applied Mathematics (2021) and a B.Phil. in Physics and Mathematics (2017), both from UWA. His research focuses on complex systems and artificial intelligence, including synchronization, unconventional computation, and optimization of complex models. Education: Doctor of Philosophy in Applied Mathematics, UWA (2019–2021) Bachelor of Philosophy in Physics and Mathematics, UWA (2017) Research Interests: Tom’s work integrates dynamical systems, Bayesian methods, and reservoir computing to address challenges in transportation modeling, machine learning, and complex systems. He explores synchronization in neural networks and applies statistical frameworks to optimize traffic systems and computational models. Key Contributions: His recent work includes Bayesian approaches for traffic parameter estimation and advancing reservoir computing’s theoretical foundations. These efforts contribute to safer transportation infrastructure and AI innovation. Awards: School of Social Sciences Award in Research Impact and Innovation (2022) Grants & Projects: Roundabout Safety Review (2025–2026): Uses drone analytics to enhance road safety. Safer Pathways (2025–2027): Improves active transport infrastructure via video analytics and community reporting. Collaborations: Works with the Planning and Transport Research Centre, collaborating on interdisciplinary projects to bridge computational theory and real-world transport challenges.
Rob Hyndman is a Professor of Statistics at Monash University , Australia, in the Department of Econometrics and Business Statistics. He is an accredited statistician with the Statistical Society of Australia and a Fellow of both the Australian Academy of Science and the Academy of the Social Sciences in Australia . As former Editor-in-Chief of the International Journal of Forecasting (2005-2018), he has shaped academic discourse in his field. Research Interests : Forecasting, time series analysis, computational statistics, anomaly detection, and exploratory data analysis with applications in energy analytics, data visualization, and hierarchical forecasting Education : Bachelor of Science (Honours) and PhD from the University of Melbourne Leadership : Director of the International Institute of Forecasters (2005-2018) His scientific contributions span business analytics, machine learning, demography, and computational statistics, with recent publications focusing on responsible forecasting, hierarchical time series modeling in emergency medical services, and advanced decomposition algorithms like MSTL. His work addresses multiple UN Sustainable Development Goals through applications in public health (forecasting pandemic cases), energy (demand forecasting), and social equity (indigenous population life expectancy estimation). He has received prestigious awards including the Moran Medal (2007, Australian Academy of Science) Pitman Medal (2021, Statistical Society of Australia) Australian Awards for University Teaching (2022) Vice-Chancellor's Award for Innovation in Learning (2020) HP Innovation Research Award (2010) Vice Chancellor's Award for Postgraduate Supervision (2008) and multiple Dean's awards. Rob has supervised over 30 PhD and Masters students while developing open-source forecasting tools and organizing annual WOMBAT workshops on business analytics.
Bonsoo Koo is an Associate Professor at Monash University's Department of Econometrics and Business Statistics within the Faculty of Business and Economics. He holds a PhD from the London School of Economics and Political Science. His research focuses on financial econometrics, econometric theory, macroeconometrics, and superannuation, emphasizing economic modelling, estimation, forecasting, and policy analysis. Dr. Koo has secured four Australian Research Council grants, including projects on superannuation sustainability, yield curve dynamics, and state-dependent fiscal multipliers. Recent collaborations span institutions globally, addressing topics like insurance market dynamics, pension planning, and macroeconomic policy impacts. He leads teams in projects such as 'High-frequency Estimation of Term Structure Models' and 'SETAR-Tree Forecasting', demonstrating interdisciplinary expertise in finance and statistics. His work contributes to UN Sustainable Development Goals through retirement income optimization and economic scenario modelling. Publications span journals like Journal of Computational and Graphical Statistics and Insurance: Mathematics and Economics , focusing on Bayesian methods, nonlinear dynamics, and stochastic pricing mechanisms.
Jonathan Keith is an Associate Professor in the School of Mathematics at Monash University. His research focuses on Bayesian modeling, computational methods, bioinformatics, invasive species modeling, and epidemiology. He develops statistical methods for detecting functional genomic elements and applies computational approaches to biological and environmental problems. His work includes Bayesian segmentation for genome analysis, agent-based models for invasive species management, and epidemiological modeling.
Dr. Elizabeth Stojanovski is a Senior Lecturer at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA) . She leads the Mathematics Education Research Group and manages the statistical consulting unit, serving industry, academics, and postgraduate students. With a PhD in Statistics from the University of Newcastle, she specializes in Biostatistics , Longitudinal Modelling , and Multivariate Statistics . Education : PhD (University of Newcastle), Bachelor of Mathematics (Honours), Bachelor of Mathematics Professional Roles : Senior Lecturer (2007–present), Research Academic (2004–2006) Memberships : Member of the Australian Statistics Society Her research spans health and education , focusing on longitudinal relationships between life events and physical/mental health. She has developed Bayesian frameworks , longitudinal models , and meta-analytic techniques for applications in tobacco control, cardiovascular disease, cancer treatment, and mathematics education. Her work includes collaborations with Eric Beh on Nobel Prize data analysis, Manohar Garg on lipid-lowering studies, and Suzanne Snodgrass on physiotherapy biomechanics. Recent publications highlight her expertise in correspondence analysis for educational and scientific data, multivariate assessment of diabetes risk , and reinforcement learning for optimization. She has supervised 15 PhD/Masters students and peer-reviewed for journals and grants. Her grants total $496,207 , including projects on statistical literacy and phytosterol efficacy. Scientific Awards : 2001 "Temporal association between life events and health" Elizabeth's teaching spans theoretical and applied statistics, with a focus on psychology, health, and business applications. She has also contributed to forensic science through cut mark analysis on bone and policy research on tobacco investments in pension funds. Her leadership in CARMA and statistical consulting underscores her interdisciplinary impact.
Enes Makalic is a Professor of Machine Learning at the Department of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, Australia. With over 15 years of experience in Bayesian inference, information theoretic statistics, and digital health, his academic career demonstrates significant interdisciplinary impact across computer science, statistics, and medical research. Faculty of Information Technology, Monash University (Current) Department of Data Science & AI, Monash University (Current) Professor Makalic completed his PhD in Machine Learning at Monash University in 2007, following a Bachelor of Computer Science (Honours) from the same institution in 2002. His research expertise spans theoretical and applied statistics, with particular focus on model selection for ultra-high dimensional statistical models, minimum message length principles of inductive inference, and applications of information theoretic statistics to epidemiology and medical imaging. His current research activities prominently feature image processing and risk prediction, with special emphasis on digital mammography and breast cancer, as well as statistical genetics and genomic prediction models for rare, polygenic diseases and traits. Professor Makalic has developed and coordinated subjects in computer science, biostatistics, survival analysis, and machine learning, and has supervised Honours, Masters, and PhD students to completion. Analysis of his recent publication trends reveals a strong focus on breast cancer risk prediction through mammographic analysis, statistical genetics, and applications of minimum message length principles. His work integrates machine learning with medical imaging to develop automated measures for breast cancer risk assessment, with significant translational potential for clinical implementation. Active reviewer and program committee member for numerous journals and conferences Recipient of research funding from NHMRC and Cancer Council Victoria Professor Makalic's research contributes to UN Sustainable Development Goals related to good health and well-being through his work in digital health and medical applications of machine learning. His projects demonstrate strong collaborative partnerships across disciplines, particularly with medical researchers focused on cancer prevention and early detection.
Daniel Schmidt is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in Bayesian inference, information theory, and statistical genomics. He holds a PhD in Computer Science (2008) and a Bachelor of Digital Systems (Honours) from Monash University (2003). His research focuses on high-dimensional Bayesian regression, Minimum Message Length (MML) principles, and applying machine learning to medical risk prediction, particularly in breast cancer via mammography analysis. He leads projects on scalable time series forecasting, quantum computing applications, and epileptic seizure prediction. Teaching commitments include developing and lecturing in units like FIT2086 (Modelling for Data Analysis) and FIT3154 (Advanced Data Analysis). He collaborates with institutions like the University of Melbourne (Adjunct Senior Research Fellow) and has contributed to open-source tools like the BayesReg package and GitHub repositories for statistical methods. His work contributes to UN Sustainable Development Goals related to health and innovation. Key research projects include efficient time series classification (MONSTER repository), seizure forecasting using EEG data, and quantum computing applications. He has authored over 119 publications, with recent work addressing adversarial attacks on time series models and Bayesian shrinkage priors for regression. Notable collaborations involve international teams in cancer genomics, statistical epidemiology, and materials science. His GitHub contributions include tools for random correlation matrix generation and fast AR model estimation.
Dr. Lan Du is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of IT. His research focuses on cross-disciplinary applications of machine learning and AI, particularly in text analytics, uncertainty estimation, knowledge distillation, and multi-modal learning. He leads projects addressing real-world challenges in public health, marketing, and clinical decision-making. Key collaborations include work with Victoria Police, Monash Health, and the National Health and Medical Research Council (NHMRC). Education: PhD in Computer Science (ANU, 2012), Bachelor of Information Technology (ANU, 2007), and B.Communication & IT (Flinders University, 2006). Research Interests: Machine/deep learning for NLP, active learning strategies, uncertainty quantification, and AI-driven solutions for healthcare and business analytics. His work bridges theoretical advancements with practical implementation, emphasizing translational research. Recent Projects (2021–2026): Includes AI models for predicting fracture outcomes (NHMRC-funded PRAISE study), risk prediction tools for pregnancy complications, and medical surveillance systems. He also collaborates on business insights derived from unstructured customer data. Teaching Commitment: Served as Chief Examiner and Lecturer for courses like FIT5149 (Applied Data Analysis) and FIT5196 (Data Wrangling). Lab/Team Involvement: Leads initiatives in AI for healthcare analytics and cross-modal learning, with active participation in Monash's research networks.
Hanlin Shang is a Professor and ARC Future Fellow at Macquarie University’s Department of Actuarial Studies and Business Analytics. He holds affiliations with the Data Horizons Research Centre, Transforming Energy Markets Research Centre, Centre for Risk Analytics, and Frontier AI Research Centre. His research focuses on functional data analysis, nonparametric and semiparametric statistics, Bayesian econometrics, computational statistics, and demographic forecasting. Education: Ph.D. in Statistics (Monash University, 2010) and Bachelor with First Class Honours in Statistics (La Trobe University, 2006). He has held postdoctoral roles at the University of Southampton and Monash University. Currently, he serves as an editor for journals like the Australian and New Zealand Journal of Statistics and Journal of Computational and Graphical Statistics . Key achievements include the ARC Future Fellowship (2024), Mollie Holman Doctoral Medal (2010), and visiting scholarships at institutions such as Columbia University and the United Nations Population Division. His research has produced over 140 publications, with recent work emphasizing mortality forecasting, functional time series analysis, and demographic modeling. Supervision spans postdoctoral researchers and PhD candidates, many of whom now hold academic or industry roles. Current projects include functional panel data analysis and feature learning for high-dimensional functional time series, supported by ARC grants. He collaborates internationally, addressing challenges in data governance, climate risk, and actuarial science.
Dr. Houying Zhu is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. Her research focuses on high-dimensional learning, statistical computing, and modelling/visualization, with an emphasis on developing efficient methodologies for modern data analysis. She holds a PhD in Applied Mathematics from the University of New South Wales and has held research roles at City University of Hong Kong and the University of Melbourne as a Maurice Belz Research Fellow in Statistics. Dr. Zhu is Co-Director of the Statistical Modelling Research Group, providing mentorship to early-career researchers and HDR students through regular supervision and collaborative projects. She actively engages in academic leadership, serving on the Board of Directors for the IASC-ARS (2023-2027) and the SSA NSW branch council (2021-2023). Her teaching spans undergraduate and postgraduate units, including Bayesian Data Analysis, Generalized Linear Models, and Statistical Graphics. Research Highlights: Innovations in quasi-Monte Carlo methods, feature selection algorithms, and medical imaging applications. Awards: Includes the President’s Award for Leadership in Statistics (2022) and multiple funding grants (MATRIX Family Funding, SSA Fellowship). Community Engagement: Organized the MATRIX Computational Mathematics for High-Dimensional Data program (Feb 2023) and contributes to statistical education initiatives. Her interdisciplinary work bridges computational statistics and real-world applications, with recent projects addressing wildfire risk modelling, protein engineering, and 6G communication systems.
