A little about me
I am Andrew Ji, an undergraduate student at the University of Virginia with a multidisciplinary background spanning climate science, artificial intelligence, and business. My academic journey across environmental science, data science, finance, economics, and machine learning has shaped a perspective that meaningful contributions require both technical rigor and a deep understanding of social, economic, and institutional dynamics.
At the center of my work is a desire to make a real and positive impact on society. I am drawn to problems that are technically challenging, socially relevant, and too complex to be addressed from a single lens. Through my studies and projects, I have come to appreciate how incentives, risks, and ESG considerations shape real-world outcomes, and how understanding these drivers is essential to turning data and models into actionable insight.
A large part of my research focuses on machine learning for extreme precipitation and climate-change attribution. I am especially interested in the tension between climate physics and AI interpretability: when a neural network predicts precipitation, what has it actually learned? Can we use models not only to forecast outcomes, but also to ask meaningful counterfactual questions about warming, circulation, and regional climate extremes? My work sits at the intersection of prediction, understanding, and decision-relevance.
More broadly, I see climate change not only as a physical process, but also as a problem of risk, incentives, and institutional action. This motivates my focus on research that is mathematically rigorous, physically grounded, and socially useful. I aim to help build a structure of knowledge that is robust enough to be trusted and practical enough to inform better decisions.
At UVA, I founded the International Student Personal Finance Club to help international students navigate the U.S. financial system, and I continue to explore ways to translate complex technical ideas into language that is understandable and actionable.
My long-term goal is to pursue graduate research in climate AI and environmental data science, building tools that make machine learning interpretable, physically aware, and relevant for understanding climate risk. Over the course of my career, I hope my work can become a small but durable part of the broader human effort to understand, improve, and responsibly shape the world we live in.