معرفی
Tim Garoni is a Professor in the School of Mathematics at Monash University, specializing in mathematical physics and statistical mechanics. His research focuses on discrete models in classical statistical mechanics, spanning mathematical physics, probability theory, combinatorics, and computer science.
Education:
- PhD in Science, University of Melbourne (awarded December 1, 2003)
- BSc (Hons) in Science, University of Melbourne (awarded December 1, 2000)
Professor Garoni's primary research interests lie in equilibrium models including the Ising model, self-avoiding walk, percolation, and the Fortuin-Kasteleyn random-cluster model, as well as non-equilibrium models such as exclusion processes. He has a particular focus on high-dimensional behavior of equilibrium models and properties of Markov chains arising in Monte Carlo methods for discrete statistical mechanics, including questions about mixing times of finite Markov chains. His work often bridges theoretical mathematics with practical applications in areas such as traffic flow modeling.
Recent publications show a strong focus on random walks, percolation theory, and high-dimensional systems, with significant contributions to understanding critical phenomena and finite-size scaling. His research also extends to transportation networks, demonstrating the interdisciplinary nature of his mathematical approaches.
Professor Garoni is actively involved in multiple research projects, including Self-Interacting Random Walks (2023-2026), Random walks with long memory (2018-2022), and the ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights (2017-2021). He is also organizing academic events such as the AIM Day: Maths AI and Energy meet Industry @ Monash in 2024.
As a supervisor, Professor Garoni accepts PhD students working on statistical mechanics and discrete probability projects. His research has resulted in over 49 publications and 8 research projects, demonstrating sustained scholarly productivity in his field.
