Research Interests

My research focuses on the intersection of advanced machine learning and real-world complex systems. I develop methodologies in Statistical Learning, Time Series Forecasting, and Spatiotemporal Modeling to address critical challenges across diverse domains.

Statistics continues to evolve in response to the increasingly complex challenges posed by modern science and industry. With the rapid growth of data across diverse domains, a central task is to uncover meaningful structure—identifying patterns, trends, and insights that help us understand what the data truly conveys. This process is often described as learning from data. In this context, statistical learning represents a broad collection of methodologies that integrate classical statistical principles with advances in machine learning to analyze and interpret complex datasets.

My research focuses on developing novel statistical methodologies for data-driven problems arising in a wide range of applied fields, including epidemiology, climate science, public health, and macroeconomics. My primary interests lie in statistical machine learning, with particular emphasis on time series forecasting, spatial data science, and imbalanced learning. I am also interested in symbolic machine learning, which seeks to discover interpretable mathematical representations and governing relationships directly from data, complementing traditional data-driven approaches with enhanced transparency and scientific insight. Through my work, I aim to contribute to both methodological advancements and impactful real-world applications.