# Statistics

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

## Researchers

**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.