NE Seminar: “Statistical and Machine Learning-enabled Modeling and Predictive Analysis in Industrial and Engineering Domains: Going Beyond Accuracy”

Details

Abstract

Data analysis and machine learning are everywhere nowadays, reshaping how we think and work across various fields. This presentation will introduce advanced statistical and machine learning-enabled modeling methodologies and predictive analysis in industrial and engineering contexts. We will explore scenarios such as deriving predictive maintenance decisions using the sensory data collected from manufacturing equipment or understanding the effects of various covariates on degradation mechanisms, such as void swelling, through data-driven modeling.

This presentation will highlight research opportunities, challenges, and advancements, especially how data science methodologies can capture the maximum benefits of collected data to not only achieve high modeling and prediction accuracy, but also extract valuable insights and basis for data-driven decision making.

Bio

Minhee Kim, Ph.D.

Assistant Professor, Department of Industrial and Systems Engineering
University of Florida

Dr. Minhee Kim is an assistant professor in the Department of Industrial and Systems Engineering at the University of Florida. She received her M.S. in Statistics and her Ph.D. in Industrial and Systems Engineering from the University of Wisconsin–Madison, respectively. Her primary expertise is in the areas of quality engineering, statistics and data analysis, with an extensive background in statistical and machine learning-based modeling and predictive analysis of engineering and industrial systems. Dr. Kim is a recipient of the Gilbreth Memorial Fellowship and the Mary G. and Joseph Natrella Scholarship. She is a co‐director of the Data Informatics for Systems Improvement and Design (DISIDE) lab at the University of Florida.