Areas of research focus in the School of Mathematics and Statistics at the University of Melbourne
The Applied Mathematics Group has interests across the fields of colloid science, medicine, chemical engineering and materials processing.
Data science combines aspects of statistics, computer science, and mathematics in order to organise, analyse, and extract knowledge from data sets.
Deep learning is a family of machine learning models based on artificial neural networks.
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, 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 is the study of the mathematics associated with models of the physical world.
Mathematics & Statistics Learning Centre
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 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 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.
This group studies a variety of areas, from the theory of branching processes to applications such as stochastic models of the stock market.