Curiosity, passion, and a desire to do better each and every day has led me here. I am Caleb Paul, and I see life as an ongoing pursuit of understanding and meaning, one shaped by learning, questioning, and steady growth over time. This perspective influences not only how I live, but also how I approach the work and challenges that matter to me.

My interests lie at the intersection of economics, data science, and artificial intelligence. I enjoy exploring complex problems, building models, and using machine learning and modern AI tools to better understand patterns in data and the systems that shape our world. For me, the goal is not only to solve problems, but to continuously grow in the process—pushing my thinking further and contributing, in small ways, to a deeper understanding of the world around us.

About MY WORK

My work sits at the intersection of economics, machine learning, and modern AI systems. I am particularly interested in how data-driven methods can be used to understand complex economic behavior, forecast markets, and support better decision-making under uncertainty. My research explores a range of techniques, from classical statistical modeling and econometrics to modern machine learning, deep learning, and large language models (LLMs). I focus on building practical models that combine economic intuition with scalable computational methods, emphasizing predictive performance, interpretability, and real-world application. More broadly, I am interested in how advances in AI can expand the toolkit available to economists and analysts, enabling new approaches to modeling markets, extracting insight from large and unstructured datasets, and automating complex analytical workflows.

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