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 examines why businesses remain in disaster-prone regions despite increasing climate risks. Using satellite imagery and business location data, it shows that firms often stay because local supplier networks, skilled labor pools, and community relationships create valuable economic advantages. Strengthening community resilience may therefore be more effective than encouraging relocation.

This research develops low-cost gallium arsenide solar-cell manufacturing to accelerate global decarbonization. Gallium arsenide absorbs light far more efficiently than silicon, potentially enabling cheaper and less capital-intensive solar production. By improving scalable manufacturing methods, the work aims to reduce the cost of expanding renewable-energy infrastructure needed to combat climate change.

This research investigates the formation and chemical composition of atmospheric aerosol particles, particularly secondary organic aerosols formed through oxidation of organic gases. Using a large controlled atmospheric chamber, the work studies how environmental conditions influence aerosol chemistry, improving understanding of air pollution, climate impacts, cloud formation, and human health effects.

This research improves climate prediction models by developing advanced computational methods for simulating cloud microphysics. By tracking more detailed information about cloud droplets and aerosol interactions, the work enhances understanding of how clouds influence Earth’s energy balance, rainfall, and climate change, helping reduce uncertainty in long-term global climate projections.

This research investigates near-wall turbulence, the chaotic fluid motion responsible for much of aerodynamic drag in transportation systems. Using high-resolution computational simulations and predictive modelling, the work aims to better understand turbulence near surfaces, enabling more efficient aerospace designs, reduced fuel consumption, and potentially major reductions in greenhouse gas emissions.

This research develops a high-resolution chemical method for analyzing tree rings to reconstruct past climates and ecosystem responses. By measuring atomic-scale chemical variations within cellulose molecules, the study separates environmental signals from biological responses, enabling more detailed understanding of historical climate change, plant physiology, and long-term ecosystem adaptation.

This research develops cavity-based methods for controlling thermal radiation by transforming random heat emission into coherent, directional thermal beams. Unlike traditional narrowband approaches, the technique enables broadband heat control using practical materials such as silicon and germanium, with potential applications in energy efficiency, waste-heat recycling, cooling technologies, and climate mitigation.