Leadership Team
Professor Di Cook
Director
Professor Brett Inder
Deputy Director and Global Development
Professor Damjan Vukcevic & Dr Minh Hyunh
Engagement & Industry Partnerships
Professor George Athanasopoulos
Cross-faculty Collaboration
Dr Kate Saunders & Dr Michael Lydeamore
Impact 2030 Themes & Responsible AI
Lab Coordinator (to be appointed)
Faculty
Monash Econometrics and Business Statistics Department has more than 40 permanent academics, and another 50 research students and post-doctoral researchers, with most pursuing research in data analytics, including open source software development. Here is just a sample of the work our academics and PhD students are involved in.
Professor George Athanasopoulos
Internationally recognised for his contributions to forecasting, time series analysis, and applied econometrics.
World leading expert in interactive and dynamic statistical data visualisation.
Expert in Bayesian statistics and econometrics developing models of financial risks and their dynamic interaction with other factors.
World leading expert in forecasting and time series modelling.
Applied statistician and educator with broad expertise, and substantail contributions to sports analytics.
Research at the intersection of mathematical optimisation, data mining and statistics.
Broadly skilled statistician and infectious disease modeller whose work spans a wide array of applications across health, biostatistics, and various non-medical fields.
Expertise on hierarchical forecasting, Bayesian model selection, and copula models.
Expert in statistical climatology, modelling climate extremes and understanding how the probability of extreme weather events.
Community builder who organises activities that promote statistical thinking and its contribution to society.
Click here to see a full list of our staff and their research interests.
Students
A few highlights of student research are:
Spatiotemporal network visualisation and modelling with applications to health data.
Preferential data, election audits, applied statistics.
Researching methodology for nonlinear dimension reduction of high-dimensional data.
Looking at high-dimensional inference, and flexible multinomial choice modeling.
Visual statistics, statistics philosophy, and computational statistics.
Data scientist, time series and forecasting, software development.
Developing methods to detect non-normal associations in high-dimensional data.
Studying AI Ethics and deep learning models
Click here for a more extensive list of our PhD students and their research interests