Uncertainty and extreme events
Rare and intermittent behavior in nonlinear dynamical systems, with an emphasis on mechanisms, statistics, prediction, and mitigation.
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Introduction
My research focuses on uncertainty quantification, nonlinear dynamics, and scientific machine learning.
Our group studies extreme events, Bayesian modelling and computation, data assimilation, stochastic dynamics, and design of experiments.
Department of Mechanical and Manufacturing Engineering
Schulich School of Engineering, University of Calgary
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Research
We connect probability, computation, and mechanics to make uncertain systems more understandable and more predictable.
Scientific machine learning
Data-driven models for dynamical systems that retain physical structure, uncertainty, and interpretable behavior.
Inference and experimental design
Bayesian computation, data assimilation, and adaptive sampling for making decisions in complex engineering systems.
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Recent work
Zero-shot prediction of thermoacoustic instability precursors via phase-space reconstruction and CNN-based recurrence learning
Proceedings of the Combustion Institute 42, 106063
PaperResonance-driven intermittency and extreme events in turbulent scalar transport with a mean gradient
Proceedings of the Royal Society A 482, 20260150
PaperDeep learning for continuous lead-time prediction of thermoacoustic instabilities in an annular combustor
Proceedings of the Combustion Institute, accepted for publication
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Current directions
Autonomous instability detection and mitigation
Learning precursors of propulsion and thermoacoustic instability, with the goal of enabling timely and reliable intervention.
Invariant-measure-informed forecasting
Forecasting chaotic systems through conservation principles and statistically accurate long-time behavior.
Adaptive decisions in high dimensions
Low-rank surrogates and adaptive data acquisition for decision making in expensive engineering systems.
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Group
Current students, postdoctoral researchers, and alumni.
Group members