This article examines how Berkeley researchers apply data science and computing to address the challenges of wildfire prediction and response. By analysing satellite imagery and environmental data through machine learning, they build models to forecast fire risk and guide emergency response. The research contributes to developing more precise early warning systems, supporting both policy decisions and public safety. The article underscores how integrating environmental science with digital tools enhances our understanding of wildfires and strengthens efforts in prevention, preparedness, and resilience in the face of climate-related disasters.

This student project explores the escalating crisis of plastic pollution using comprehensive global datasets. It applies data visualisation and statistical analysis techniques to examine geographic trends, sources of ocean contamination, and the correlation between consumer behaviour and waste accumulation. The blog highlights how plastic production and inadequate recycling systems contribute to environmental degradation. It further outlines potential policy interventions such as bans, taxes, and extended producer responsibility. By leveraging data insights, the project strengthens the case for evidence-based advocacy, public awareness campaigns, and long-term environmental protection strategies targeting plastic waste reduction.