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- Liam Hodgkinson
Dr Liam Hodgkinson
The University of Queensland
Liam Hodgkinson was a PhD student at the University of Queensland studying applied probability, graduating in November 2019. He began his Bachelor of Science with Honours degree at the University of Queensland in 2012 with an Australian Student Prize and a UQ Excellence Scholarship. Since then, he has been awarded the James Cecil Stevenson Memorial Prize in 2013, the Ethel Raybould Memorial Prize in 2016, and has earned seven Dean's commendations. His interests include the convergence of stochastic processes, stochastic partial differential equations, and interacting particle systems and large-scale simulations thereof. His current work involves the analysis and approximation of particle systems arising in ecology and epidemiology. Liam is currently a postdoctoral research fellow within the Department of Statistics at UC Berkeley.
Research Interests:
Applied probability
Interacting Particle Systems
mathematical biology
Spatial Epidemiology
stochastic partial differential equations
Stochastic Processes
Stochastic simulation
Qualifications:
Bachelor of Science (Hons)
PhD in Mathematics
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
Hodgkinson, L., van der Heide C., Roosta F., & Mahoney M.
(2021). Stochastic Continuous Normalizing Flows: Training SDEs as ODEs.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. 161, 1130–1140.
Pollett, P., & Hodgkinson L.
(2021). Population networks with no occupancy ceiling.
ANZIAM 2021.
Roosta, F., Hodgkinson L., van der Heide C., & Kroese D.
(2021). Shadow Manifold Hamiltonian Monte Carlo.
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. 130, 1477-1485.
Pollett, P., & Hodgkinson L.
(2020). High-density limits for infinite occupancy processes.
64th Annual Meeting of the Australian Mathematical Society.
McVinish, R., & Hodgkinson L.
(2017). Fast simulation of metapopulation models.
Melbourne-Singapore Probability and Statistics Forum.
Journal Articles
Hodgkinson, L., Salomone R., & Roosta F.
(2021). Implicit Langevin Algorithms for Sampling From Log-concave Densities.
Journal of Machine Learning Research. 22(136), 1-30.
Research links
