Abstract

Abstract

This is my introductory talk to EEG , split into two parts. In the first part, I’ll share insights from my PhD research on pangolin exploitation and illegal hunting. The second part will focus on my current work, which explores strategies for crime prevention in protected areas.

Ranger patrols are a critical element of threat management in these areas, yet the resources available to rangers are often insufficient for achieving optimal protection. My current research seeks to enhance patrol effectiveness by analyzing spatial, long-term data from rangers across eight protected areas, alongside spatial data on hunter behavior around these areas.

During my talk, I’ll outline my analytical plans, and I would value your feedback on machine learning-based approaches. Developing machine learning expertise is one of my key goals during this fellowship, and I’m keen to explore how these techniques can advance this research.

Bio

Charles is a 2021 National Geographic Explorer and 2024 Schmidt Science Fellow at the Department of Computer Science and Technology (University of Cambridge) and John A. Paulson School of Engineering and Applied Sciences (Harvard University). His postdoctoral research focuses on crime prevention in national parks, leveraging machine learning and spatial data on poaching gathered by rangers to enhance the effectiveness of ranger patrols. Charles has a background in biodiversity conservation. He recently completed a PhD in Zoology from the University of Cambridge (funded by Gates Cambridge Trust) on the dynamics of threats to pangolins.