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UC Berkeley Professor Joshua Blumenstock combines machine learning and AI to address global development challenges, focusing on reducing extreme poverty. His research, rooted in computer science and quantitative analysis, emerged during his graduate studies at UC Berkeley, where he identified new ways to apply technical tools to long-standing questions about poverty alleviation. Blumenstock’s work spans countries like Afghanistan, Togo, and Rwanda, where he applies AI to identify households most in need of humanitarian aid and improve access to financial services.

In a recent video as part of UC Berkeley’s 101 in 101 series, Blumenstock explains the estimated financial cost required to end extreme global poverty. The video highlights how AI-driven analysis can provide a clearer picture of poverty levels and the resources needed to address them. Blumenstock serves as a professor at the School of Information and Goldman School of Public Policy, while also directing the Global Opportunity Lab and co-directing the Center for Effective Global Action.

Blumenstock’s approach leverages data to enhance the effectiveness of aid distribution and financial inclusion programs. By analyzing patterns in low-income settings, his team identifies gaps in access to services and opportunities, enabling more targeted interventions. His work extends to responding to climate shocks and other crises, where AI helps prioritize responses based on need.

The 101 in 101 video series features UC Berkeley faculty and experts explaining their research in concise, accessible formats. Blumenstock’s contribution focuses on the intersection of technology and global development, demonstrating how AI can be a practical tool for solving complex humanitarian challenges. The series aims to make academic insights more widely understandable to the public.