Statistics in the Mix: Navigating Interdisciplinary Challenges in Data Science

In the modern era of data-driven discovery, statistics stands at the core of interdisciplinary research, powering advancements across a wide range of domains, such as biomedical sciences, environmental studies, social sciences, and beyond. This session, "Statistics in the Mix: Navigating Interdisciplinary Challenges in Data Science," brings together innovative statistical methodologies designed to tackle real-world complexities at the intersections of diverse fields. It highlights a broad spectrum of recent developments, including tree-based methods for confounding control in complex biomedical settings; statistical support for environmental sciences, such as climate change modeling, air quality forecasting, and ecosystem service evaluation; semiparametric estimation with neural networks, enabling valid and interpretable inference on parameters of interest; and interdisciplinary data science driven by large-scale statistical consulting efforts. By emphasizing the unifying role of statistical thinking, the session illustrates how statistical innovation not only fosters meaningful cross-disciplinary integration but also continues to serve as a methodological engine and collaborative bridge in modern scientific inquiry.