Statistical Learning and Inference: Recent Developments and Geoscience Applications

Abstract

This report presents recent progress in statistical methodology and geoscience applications from our research group. Methodological topics include structure-preserving visualization, Bayesian inference, personalized functional principal component analysis, and statistical and deep learning approaches to signal analysis. Applications span oceanographic structure and hydrographic transitions, groundwater variations and seismic activity, aftershock modeling, and distributed acoustic sensing. Together, these studies highlight the role of statistical methods in understanding complex scientific data.

線上視訊請點選連結