
Liam Blake
Higher Degree by Research Candidate
School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and Technology
I am a doctoral candidate working on developing new schemes for data assimilation in models with unresolved components. My research is supported by the Constance Fraser Scholarship from the University of Adelaide, and the AF Pillow Top-Up Scholarship from ANZIAM.
My Master of Philosophy research project worked on improving the predictions differential equation models by accounting for inevitable uncertainty and errors in a practical and computationally efficient way. The project was motivated by challenge in various application domains, including climate modelling, oceanography, epidemiology, and fluid mechanics. More broadly, I am interested in problems that lie on the interface between statistics, stochastic processes, and dynamics to draw from my diverse mathematical background.
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Education
Date Institution name Country Title 2025 The University of Adelaide / Adelaide University Australia Doctor of Philosophy 2022 - 2024 The University of Adelaide Australia Master of Philosophy 2018 - 2021 The University of Adelaide Australia Bachelor of Mathematical Sciences (Advanced)
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Journals
Year Citation 2025 Blake, L. A. A., Maclean, J., & Balasuriya, S. (2025). Rigorous convergence bounds for stochastic differential equations with application to uncertainty quantification. Physica D Nonlinear Phenomena, 481, 134742.
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Theses
Year Citation 2024 Blake, L. (2024). Computable Characterisations of Uncertainty in Differential Equations. (Master's Thesis, The University of Adelaide). -
Preprint
Year Citation 2024 Blake, L., Maclean, J., & Balasuriya, S. (2024). Unifying Lyapunov exponents with probabilistic uncertainty
quantification.2024 Blake, L., Maclean, J., & Balasuriya, S. (2024). Rigorous Convergence Bounds for Stochastic Differential Equations with Application to Uncertainty Quantification.
DOI2023 Blake, L., Maclean, J., & Balasuriya, S. (2023). The convergence of stochastic differential equations to their
linearisation in small noise limits.
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