Tim received his Doctorate from the University of Melbourne in 2003, and then held postdoctoral positions at the University of Minnesota, New York University, and the University of Melbourne. He joined Monash University in 2011, where he is currently an Associate Professor in the School of Mathematical Sciences. Tim’s research interests are chiefly in the application of Markov-chain Monte Carlo methods to problems in statistical mechanics, especially to the study of phase transitions. This involves developing, and rigorously analyzing, Monte Carlo algorithms for studying discrete/combinatorial models in equilibrium statistical mechanics. It also involves studying systems far from equilibrium, such as traffic models.
A fundamental challenge in constructing Big Models is the question of calibration. Big Models typically have a large number of parameters, which need to be inferred from data in a robust and theoretically justifiable way. Approximate Bayesian Computation is one promising approach to tackle this calibration problem for large complex models.
Statistical mechanics is that branch of mathematical physics which seeks to explain how macroscopic behaviour can emerge from the interactions between a large number of microscopic particles; in short, to explain how and why the sum is greater than its parts.