If you are an Advanced Mathematics or Advanced Science student, then Honours is built into your program. For all other students, if you are keen on mathematics and statistics and have achieved good results in years 1 to 3, you should consider embarking on an Honours year.

Information on Statistics Honours, including program details, potential research projects and application forms, can be found in the Handbook of Honours in Statistics.

For other information about doing Honours in Statistics, see the Honours Page.

Honours Coordinator - Statistics

If you have any questions about the Honours year, please don't hesitate to contact Gery.

Dr Gery Geenens

E: ggeenens@unsw.edu.au
T: 9385 7032
Office: H13 Lawrence East 2053

Statistics project areas

The following are suggestions for possible supervisors and honours projects in statistics. A more comprehensive description of the offered projects is available in the Handbook of Honours in Statistics. Other projects are possible, and you should contact any potential supervisors to discuss your options.

  • Boris Beranger

    • Analysis of extremes: theory, computations, environmental applications
    • Big and Complex Data analysis

    Zdravko Botev

    • Kernel Density Estimation
    • Multilevel and Markov Chain Monte Carlo Methods for Rare-Event Simulation
    • Monte Carlo Methods in Network Reliability

    Leung Chan

    • Mathematical finance

    Feng Chen

    • Inference and applications of stochastic processes, especially point processes

    Diana Combe

    • Combinatorial designs, graph theory and graph labellings

    Yanan Fan

    • Bayesian statistics
    • Markov chain Monte Carlo

    Gery Geenens

    • Statistical dependence: modelling, quantification and analysis
    • Copula modelling: theory and applications
    • Nonparametric and semiparametric estimators for probability densities and regression functions
    • Sports analytics (mainly football/soccer)

    Ben Goldys

    • Stochastic differential equations

    Pavel Krivitsky

    • Social network analysis
    • Statistical computing

    Pierre Lafaye de Micheaux

    • Dependence Measures
    • NeuroImaging
    • Big Data and Internet of Things
    • Statistical Inference for Complex Random Vectors

    Libo Li

    • General theory of stochastic processes and its applications in financial mathematics

    Sarat Moka

    • Fast and efficient model selection for high-dimensional data.
    • Development of efficient estimation and sampling algorithms for random graphs and spatial point processes.
    • Development of model compression methods for deep neural networks.

    Jake Olivier

    • Statistical methods for the analysis of count data with applications to epidemiology and population health data

    Spiro Penev

    • Wavelet methods in non-parametric inference
    • Latent Variable Models

    Donna Salopek

    • Financial modelling
    • Fractional Brownian motion

    Scott Sisson

    • Bayesian computational techniques
    • Models of extremes of climate processes
    • Genetic epidemiology

    Eva Stadler (The Kirby Institute)

    • Statistical analysis of data relating to infectious diseases (malaria or COVID-19)

    Jakub Stoklosa

    • Analysis of capture-recapture data and estimation of animal abundance
    • Measurement error modelling
    • Non-parametric smoothing

    David Warton

    • Ecological statistics: see UNSW Eco-Stats Opportunities
    • High-dimensional data analysis
    • Species distribution modelling