Modeling Complex Spatio-Temporal Processes
My research focuses on Bayesian modeling of spatio-temporal processes, with applications in environmental science and public health. I develop methods to address spatial and temporal dependence and uncertainty, as seen in my work on sea level change, RSV forecasting, and temperature changes relating to urbanization. My models are designed to extract meaningful patterns from noisy, high-dimensional data, supporting timely, data-informed decision-making.
Developing Interpretable Methods for Spatial Analyses
I design spatial modeling techniques that emphasize clarity and interpretability, helping researchers draw meaningful conclusions from complex data. My recent work examines spatial confounding by distinguishing between spatial structure and covariate relationships in a transparent way. I also develop methods that reduce high-dimensional spatial signals into components that are easier to understand and communicate in applied contexts.
Cross-Disciplinary Collaboration and Impact
My research is grounded in collaboration, often connecting statistical methodology with real-world challenges across disciplines. I have partnered with scientists and researchers on projects involving satellite aerosol data, harmful algal blooms, and infant respiratory health. My work is supported by agencies like NIH and DOE, and I invest deeply in student mentorship, regularly co-authoring with trainees and guiding interdisciplinary research teams.