Ramit Debnath
Papers
1
Total Citations
110
H-Index
1
About
Ramit Debnath is a leading researcher at the intersection of climate policy, artificial intelligence, and computational social science. His work focuses on how machine learning and natural language processing can be harnessed to analyze and improve public policy, particularly in the context of sustainability and global crises. Debnath’s most cited paper, “India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling” (2020, 110 citations), exemplifies his approach. In this study, he used advanced topic modelling to dissect India’s reactive policy responses during its unprecedented lockdown of 1.3 billion people—a move that cost an estimated USD 98 billion. By revealing how the government pivoted across sectors under immense pressure, Debnath highlighted the critical role of data-driven analysis in crisis management. His broader contributions include pioneering methods to quantify climate policy impacts and social costs, earning him recognition as a Cambridge Zero Fellow and a Schmidt Science Fellow. With a growing body of work that bridges technical rigor and real-world relevance, Debnath is shaping how researchers and policymakers understand complex socio-environmental systems.
Research Focus
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Top Papers
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