Statistics

Listed on this page are current research projects being offered for the Vacation Scholarship Program.

For more information on this research group see: Statistics

Classifying phases of many-body systems using machine learning

(Posted for 2019-2020)

Matter can exist in various different phases. Water for instance can exist in a frozen, a liquid or a gaseous state depending on external parameters such as temperature and pressure. Other materials may exhibit a very complicated phase diagram involving lots of parameters and many distinct phases, potentially even phases of topological origin. When looking at a specific Hamiltonian describing the dynamics of a classical or quantum system with a large number of particles it is usually highly non-trivial to determine the phase the system resides in for a given set of parameters.

In this project the vacation scholar will explore how to describe phases of matter mathematically and use machine learning techniques to map out the phase diagrams of some model systems. Affinity to physics and basic programming experience will be assumed but besides numerical work (with Python) there will also be ample opportunity to gain new analytical insights.

Contact: Thomas Quella Thomas.Quella@unimelb.edu.au

Statistical analysis of large-scale metabolomics data

(posted for 2016-2017)

This work will deal with statistical issues regarding handling unwanted variation in the presence of missing values in high-dimensional metabolomics data - a relatively new field in omics. Two student projects are available. The first project will assist with the development of a software package for the analysis of metabolomics data using R (https://www.r-project.org/). The second project will evaluate some of the recently developed statistical approaches for the analysis of metabolomics data in terms of handling unwanted variation and missing values. Both projects will engage directly with actual analyses using metabolomics datasets. The candidates will be based at the Centre of Epidemiology and Biostatistics in the Melbourne School of Population and Global Health, and involve collaborations with the Victorian Centre for Biostatistics and the Speed Lab, Walter and Eliza Hall Institute of Medical Research. Experience with the R software is a pre-requisite for both projects.

Contact: Alysha De Livera alyshad@unimelb.edu.au