This research develops bio-inspired autonomous underwater vehicles by combining evolutionary simulation with physical prototype testing. Optimising swimmer shape and motion through an iterative feedback loop enables more energy-efficient AUVs capable of long-range ocean monitoring, supporting environmental observation, infrastructure inspection, and maritime surveillance across Canada's vast marine environments.

This research explores tidal energy as a reliable renewable source using digital twin technology. By simulating tidal farms in the Long Island Sound, it evaluates performance and environmental impacts before construction. The approach enables efficient, fish-friendly energy design, offering a scalable solution for sustainable ocean-based power generation worldwide.