*First Talk* *Title* Identifying Key Countries in the Illegal Elephant Ivory Trade Network *Abstract* Illegal wildlife trade is a key driver of biodiversity loss, but targeting policy to maximise disruption to trade remains a key challenge. A network approach was applied to seizure data to prioritise national action disrupting the illegal trade of elephant ivory. By simulating the removal of countries from trade, targeting groups of countries was found to be most effective due to network redundancy. Despite temporal variability, trade was highly concentrated and cessation in less than 10 countries would have disrupted 75% of trade in 2018-2020. These findings support evidence-based legislation and efficient allocation of conservation resources for tackling illegal wildlife trade. *Bio* Jakob is a PhD student in the Conservation and Development Lab (Department of Geography). His research focuses on evaluating policy for sustainable land systems, supervised by Prof. Rachael Garrett and Prof. Srinivasan Keshav. This work is supported by the Centre for Doctoral Training on Artificial Intelligence applied to the study of Environmental Risk (AI4ER CDT). Before starting his PhD, Jakob completed an MRes with AI4ER in Environmental Data Science, where he collaborated with TRAFFIC to develop data-driven tools to inform international illegal wildlife trade policy. Previously, Jakob completed an undergraduate degree in Natural Sciences at the University of Cambridge, specialising in Plant Sciences, and contributed to research on metrics for biodiversity offsetting, novel approaches to wildlife monitoring and forest ecology. *Second Talk* *Title* Machine Learning for Building-Level Heat Risk Mapping *Abstract* Climate change is intensifying the frequency and severity of heat waves, increasing risks to public health and energy systems worldwide. However, many existing heat vulnerability assessments focus primarily on outdoor temperatures, overlooking indoor conditions that directly affect occupants. Although building simulations can reveal the types of buildings whose occupants are most at risk, they rarely pinpoint the exact locations of these vulnerable buildings. In this presentation, I will present a data-driven workflow that locates high-risk buildings and discuss the labeling strategies we have explored for classifying real-world structures using satellite imagery. *Bio* Andrea is a first-year PhD student in the Department of Computer Science and Technology at the University of Cambridge. She is supervised by Prof Srinivasan Keshav. Her research bridges machine learning with civil and environmental engineering, focusing particularly on its applications within the built environment.
