This research uses LiDAR and individual tree segmentation to replace traditional polygon-based forest inventories with precise, tree-level data. By modelling the growth and interactions of individual trees, it enables more accurate forest management, improving timber planning, ecosystem resilience, and climate adaptation while supporting sustainable forestry across British Columbia.

This research investigates how coccolithophores—microscopic marine algae that both absorb and release carbon dioxide—have influenced Earth's carbon cycle over the past three million years. Using fossil sediments, geochemistry, and machine learning, it reconstructs past ocean ecosystems to improve predictions of how marine carbon cycling will respond to future climate change.

This research reconstructs 200 years of El Niño–Southern Oscillation (ENSO) variability using oxygen isotope records preserved in corals from Christmas Island. By combining coral archives, modern ocean observations, and climate models, it improves understanding of how ENSO is responding to anthropogenic climate change and enhances predictions of future climate extremes.

This research improves flood prediction by analysing data from more than 3,000 rivers worldwide and using local fitting techniques to compare similar weather events. By relying on relevant historical data rather than human intuition, the model aims to produce more accurate flood forecasts and strengthen disaster preparedness under climate change.

This research uses functional regression to forecast how climate change will affect electricity demand across California. By modeling complete demand patterns rather than isolated data points, it aims to help design smarter, more resilient, and more equitable power grids that reduce outages during increasingly frequent heatwaves and extreme weather.

This research develops intelligent polymer membranes that selectively capture carbon dioxide using molecular simulations to design highly efficient gas-separation materials. By improving carbon capture at industrial sources, the technology could reduce greenhouse gas emissions, support cleaner energy systems, and contribute to tackling one of the world's greatest challenges: climate change.

This research develops a low-temperature carbon-capture material that uses waste heat from solar panels to release captured CO₂. By reducing energy requirements from hundreds of degrees to just 70°C, the technology offers a more sustainable, scalable, and grid-independent approach to carbon capture and long-term climate-change mitigation.

This research investigates how freshwater organisms respond to climate extremes such as warming rivers and drought. Using field surveys, experiments, and modelling, it examines whether species can adapt to higher temperatures and what costs that adaptation may carry. Understanding these limits is crucial for protecting ecosystems, water security, and biodiversity.

Using honeybee communication and disease defense as a framework, this research explores how early warning signals can improve wildlife conservation. By examining indicators of ecosystem health, climate-driven parasite dynamics, and preventative monitoring strategies, it argues that detecting subtle ecological changes early is essential for protecting biodiversity and ecosystem resilience.

This research investigates feronia, a plant protein essential for heat adaptation. By studying how feronia regulates auxin signaling and plant growth under temperature stress, the work aims to uncover mechanisms that could support the development of heat-resilient crops, improving agricultural productivity and food security in a warming global climate.