Biodiversity Monitoring

In this episode, we talked shop with Hager Radi about her biodiversity monitoring work. While biodiversity modeling may sound simple, count organisms and mark their location, there is a lot more to it than that! Incomplete and biased data can make estimations hard. There are also many species with very few observations in the wild. Using machine learning and remote sensing data, scientists can build models that predict species distributions with limited data. Listen in and hear about Hager’s work tackling these challenges and the tools she has built.

Guest

Hager Radi: Hager (ha-ga-r) is a Senior Applied Research Scientist at Mila - Quebec Artificial Intelligence Institute. She has MSc in Computing Science from University of Alberta, focused on Reinforcement learning, as part of IRL Lab , also at RLAI lab and Amii. Her research expertise spans: reinforcement learning, computer vision, LLMs and Agentic AI and Remote sensing. More broadly, she is passionate about applied research with a strong focus on advancing AI for good.