Research in the field of statistics from the Faculty of Science, University of Melbourne.


David Balding     Application of mathematical, statistical and computational methods in genetics (population, evolutional, medical and forensic genetics) and related areas of biology.

Howard Bondell     Variable and model selection, robust estimation, quantile regression, nonparametric smoothing and regression, regularization and Bayesian methods.

Yaoban Chan     Statistics and mathematical biology.

Tingjin Chu     Spatial statistics.

Sandy Clarke-Errey     Multiple hypothesis testing procedures in the presence of dependence.

Aurore Delaigle     Nonparametric estimation, measurement errors, deconvolution problems and functional data analysis.

Sue Finch     Applied statistics for statistical education.

Mingming Gong     Machine learning, causal reasoning, computer vision

Ian Gordon     Statistical application in consulting, data analysis, meta-analysis, survival analysis and statistical education.

Graham Hepworth     Application of statistics in group testing, discrete interval estimation and statistical consulting.

John Holmes     Statistics, statistical genetics.

Wei Huang     Nonparametric regression, functional data, missing data.

Mario Kieburg     Harmonic analysis and group and representation theory, random matrix theory, orthogonal functions and polynomials, quantum field theory, telecommunications systems, supersymmetry & graded algebras, quantum chaos, quantum information theory.

Pavel Krupskiy     Nonparametric statistics, copulas, multivariate extremes.

Kim-Anh Le-Cao     Biological data integration, multivariate projection-based methods, computational statistical learning, R software development.

Stephen Leslie     Statistical genomics, including detecting and controlling for population differences in genetic data, typing complex genetic variation, and statistical analyses of the relationship of genetic variants to disease.

Dennis Leung     Graphical models, high-dimensional statistics

Robert Maillardet     Learning and teaching innovation, statistics.

Cameron Patrick     Applied statistics, particularly ecological modelling, spatial data and data visualisation.

Liuhua Peng     Statistics.

Guoqi Qian     Statistics theory, biostatistics, bioinformatics, computational statistics and mathematical methods for climatology, ecology and the environment.

Andrew Robinson     Applied statistics, sampling theory, environmental and ecological statistics, forest biometrics, mixed-effects models, model validation.

Heejung Shim     Data science, Bayesian statistics, computational biology, statistical genomics, stochastic processes, machine learning, applied statistics.

Damjan Vukcevic     Biostatistical research with a specialisation in statistical genetics.

Susan Wei     Statistical infernce for big data, machine learning, data science.

Research centres

ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
Brings together Australia's best researchers in applied mathematics, statistics, mathematical physics and machine learning.

Centre of Excellence for Biosecurity Risk Analysis (CEBRA)
With our expansive borders and proximity to Asia, implementing effective biosecurity policies and management tools is essential to protecting our unique ecosystems.

Mathematical Research Institute MATRIX
MATRIX is an international research institute that runs research programs where world lead researchers in the mathematical sciences, as well as experts from business and industry, can come together.

Melbourne Centre for Data Science
An interdisciplinary environment set up to lead advances in data science for the benefit of society.

Melbourne Integrative Genomics (MIG)
We aim to understand biological systems, with a focus on genomes as the blueprint for each system. We are interested in biological systems of different scales.

Statistical Consulting Centre
The Statistical Consulting Centre provides statistical services to business, industry, government and the academic world.

SWARM Project
The SWARM Project is attempting to achieve fundamental advances in collaborative reasoning with a focus on improving intelligence analysis.

Research in this area is conducted in the School of Biosciences and School of Mathematics and Statistics.