Research groups

Areas of research focus in the School of Mathematics and Statistics at the University of Melbourne

• Applied Mathematics

  The Applied Mathematics Group has interests across the fields of colloid science, medicine, chemical engineering and materials processing.

• Data Science

  Data science combines aspects of statistics, computer science, and mathematics in order to organise, analyse, and extract knowledge from data sets.

• Deep Learning

  Deep learning is a family of machine learning models based on artificial neural networks.

• Discrete Mathematics

  Discrete mathematics is the study of mathematical structures that are by nature discrete rather than continuous. It includes combinatorics and graph theory.

• Learning and Teaching Innovation

  Innovation in the teaching of mathematics and statistics is a key focus of the department. This group fosters innovations in learning and teaching for tertiary mathematics and statistics.

• Mathematical Biology

  Mathematical, statistical and computational methods are crucial in many areas of modern biological research. Conversely technological advances in biology allow more data, often of a novel type or at a finer resolution, to be collected resulting in new challenges that are motivating research in mathematics, statistics and computational methods.

• Mathematical Physics

  Mathematical Physics is the study of the mathematics associated with models of the physical world.

• MSLC

  Mathematics & Statistics Learning Centre

• Operations Research

  Operations Research (OR) provides a scientific approach to decision making. It involves formulating mathematical models of these problems, and developing mathematical tools to obtain solutions.

• Pure Mathematics

  Pure math is the study of the intrinsic concepts and structural properties underlying modern mathematics. Its purpose is to search for a deeper understanding and an expanded knowledge of mathematics itself.

• Statistics

  Statistics is the science of collecting, organising and interpreting data. Mathematical descriptions of the data collection processes and the statistical analysis are used to determine accurate statistical methods.

• Stochastic Processes

  This group studies a variety of areas, from the theory of branching processes to applications such as stochastic models of the stock market.