Academic journey & research vision.
I am a PhD candidate in the Department of Mechanical Engineering at the University of Victoria, supervised by Prof. Caterina Valeo. My research investigates how low impact development (LID) infrastructure and physically based hydrological models, complemented by data‑driven methods, can improve urban stormwater systems and climate resilience.
My current projects combine fuzzy entropy and process‑based hydrological simulation with large language models to quantify uncertainty in spatial datasets and provide decision support for municipalities planning sustainable drainage infrastructure.
I am especially interested in reducing uncertainty in physically based models through better input representation and rigorous validation campaigns.
Today my research agenda spans generative AI, physics-informed neural networks for hydrology—particularly peak flow prediction—and climate change adaptation topics that push water infrastructure toward resilient, low-carbon futures.
Doctor of Philosophy (PhD, in progress), Mechanical Engineering
Hydrology, stormwater management, fuzzy entropy, uncertainty quantification
Grade: 86.8 average
PhD candidate under the supervision of Prof. Caterina Valeo focusing on peak-flow prediction, fuzzy entropy methods, spatial data uncertainty, LID, and integrating AI with hydrological modelling frameworks.
Master of Applied Science (MASc), Safety and Risk Engineering
Grade: 84.4 average
Conducted research in safety systems analysis, probabilistic engineering methods, environmental risk assessment, and broader risk-informed design.
