Professor Michael Small

Prof. Michael Small is an applied mathematician. His work seeks to use data to understand the evolution, structure and behaviour of complex systems. Complex systems are characterised by a large number of interacting components - each of which might be quite simple, but which collectively lead to emergent behaviour. That is, the whole is greater than the sum of the parts and has properties that would not be easily anticipated.

Applications of Michael's work are very broad - transmission of infectious disease in heterogeneous populations; predictive and cascading failure in engineered systems; mental health and wellbeing; information propagation, and patient treatment in hospitals; bioinformatics, cellular communication, protein interaction, and drug design; and, novel methods in machine learning.

Within the mathematics and physics communities, Prof. Small is known for his work on data driven methods for reconstruction of chaotic dynamical systems from data; neuronal networks and reservoir computing; and, propagation and collective dynamics of networked systems.