分野別セミナー

概要: In this presentation, Dr. Yvette Everingham will share insights from three distinct yet interconnected areas of her research, showcasing a comprehensive approach to advanced data analytics. The talk will delve into novel data compression methods for feature extraction within statistical learning applications, demonstrating how efficient data representation can enhance model performance and interpretability. Following this, Dr. Everingham will overview her work in predictive data analytics, encompassing statistical modelling and machine learning, highlighting innovative techniques for forecasting and pattern recognition across diverse datasets. Finally, the presentation will cover multi-model fusion strategies, illustrating how combining outputs from multiple predictive models can lead to more accurate predictions. Collectively, these research areas offer a holistic perspective on leveraging cutting-edge methodologies to extract meaningful insights from complex data, driving advancements in scientific discovery and practical applications.

略歴: Yvette is a specialist in multi-model data fusion who is dedicated to (i) identifying strategies that will deliver better student learning outcomes in STEM education, and (ii) researching agtech solutions to help agricultural industries increase profits with a smaller environmental footprint under challenging climates.

Currently, Yvette holds positions as Director of AgTAC (Agriculture, Technology and Adoption Centre) and Professor in Data Science at James Cook University. Previous positions include Climate Impacts Scientist (CSIRO), Associate Dean Graduate Research Studies (JCU), Focus Area Chair for Agricultural Weather and Climate Services (World Meteorological Organisation, Geneva) and Associate Editor for the Journal of Agronomy for Sustainable Development.