Grants and Contributions

This project aims to integrate existing and novel SEO biodiversity products into a multimodal machine learning (MM-ML) model to assess ecosystem recovery, validated through two boreal forest case studies focusing on woodland caribou and birds.

This project aims to integrate SEO data with AI into a novel dashboard to track biodiversity and climate variables (EBVs and ECVs) to make climate and biodiversity monitoring more scalable and automated.

Multi year

This project aims to develop an AI-powered system using satellite imagery and remote sensing technology to monitor biodiversity and track ecosystem changes in New Brunswick, including habitat features, forest regrowth, and the impacts of ecosystem management and climate change.

This initiative aims to develop cost-effective monitoring tools for Northern Canada’s ecosystems, collaborating with government and Indigenous partners to track climate and biodiversity impacts and guide future SAR land monitoring strategies.

This project aims to develop the DeCAF-RRT, a wide-area monitoring tool using satellite Earth observation (SEO), to support salmon habitat monitoring. Effective monitoring promotes healthy ecosystems, sustainable biodiversity, and stable salmon populations for food and economic security.

This project aims to integrate SEO data into spatially explicit bird abundance models to enhance biodiversity conservation and land-use planning in Canada.

The project involves working on machine intelligence and new concepts for weeding attachments to improve robotic agricultural equipment for automated mechanical weeding of root vegetable crops.

This project will develop a robust human cell culture-based pipeline for engineering innate radiation protection mechanisms. As opposed to traditional models which lack biological relevance, this proposal leverages the use of hPSCs and derived cells thereof, constituting both a sensitive and relevant approach. The project will identify potential gene therapy targets that can confer radioprotective phenotypes by effectively reducing oxidative stress, DNA damage, and cell death responses whilst deepening our understanding of their underlying mechanisms. This approach could significantly advance efforts to protect human health in the challenging environment of space. The data generated herein is a potentially invaluable resource for the Canadian Space Agency and others for prediction of radiation resistance factors in living systems. Finally, this project will engineer gene therapy delivery vectors to demonstrate effective radiation protection in target cell culture models relevant for spaceflight environments.

Publication of a bilingual IP best practices guide for the AI era, organization of public legal education workshops, and offering of pro bono IP services to Canadian tech companies