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.
Richard Brak Enumerative combinatorics, statistical mechanics, stochastic processes, critical phenomena (phase transitions), Markov processes, combinatorics, orthogonal functions and polynomials, polymers, modelling of biological systems.
Tim Brown Statistics and stochastic processes
Yaoban Chan Statistics and mathematical biology.
Tingjin Chu Spatial statistics.
Sandy Clarke 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.
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.
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.
Robert Maillardet Learning and teaching innovation, statistics.
Rheanna Mainzer Statistics and learning and teaching innovation.
Liuhua Peng Statistics.
Julia Polak 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.
Zhuosong Zhang Statistics.
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.
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.
The SWARM Project is attempting to achieve fundamental advances in collaborative reasoning with a focus on improving intelligence analysis.
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.
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.