Honours in Statistics

Boris Beranger, opens in a new window

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

Zdravko Botev, opens in a new window

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

Leung Chan, opens in a new window

• Mathematical finance
  

Rohitash Chandra, opens in a new window 

• Bayesian deep learning with variational inference
• MCMC sampling
• Bayesian data imputation for medical and climate data

Feng Chen, opens in a new window

• Inference and applications of stochastic processes, especially point processes

Yanan Fan, opens in a new window

• Bayesian statistics
  
• Markov chain Monte Carlo

Gery Geenens, opens in a new window

• 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, opens in a new window

• Social network analysis
  
• Statistical computing
• Surveys and sampling

Pierre Lafaye de Micheaux, opens in a new window

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

Libo Li, opens in a new window

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

Sarat Moka, opens in a new window

• 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.
• Machine learning prediction of bluebottles presence along the Australian coast
• Statistical and Experimental approach to estimation of tyres in a stockpile
• Deep learning models for spatio-temporal data

Jake Olivier, opens in a new window

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

Sahani Pathiraja , opens in a new window

• Generative modelling and sampling methods (incl. sampling for Bayesian inference, sequential Monte Carlo)
• Stochastic differential equations and their applications
• Analysis of modern machine learning techniques
• Stochastic hydrology

Spiro Penev, opens in a new window

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

Donna Salopek, opens in a new window

• Financial modelling
  
• Fractional Brownian motion

Scott Sisson, opens in a new window

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

Jakub Stoklosa, opens in a new window

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

David Warton, opens in a new window

• Ecological statistics: see UNSW Eco-Stats, opens in a new window Opportunities
  
• High-dimensional data analysis
• Species distribution modelling

Atefeh Zamani, opens in a new window

• Time series analysis
• Functional data analysis