Session: Past and Future of Process/Product Analytics & Machine Learning, including Education and Workforce Development
Author: John Hedengren, Brigham Young University
Title: Data-Driven Engineering Education with Hands-On Learning
Data engineering with acquisition, transport, curation, and storage of data has emerged as a key role in Industry 4.0. Aspiring data scientists have many online repositories and extensive example datasets that demonstrate principles of supervised and unsupervised learning. These datasets are often curated to present sanitized data sets to focus on the applications of classification and regression. This presentation shares new resources for hands-on data-driven engineering to teach aspiring data scientists important data engineering skills in collection, cleansing, pipelines, storage, and quality assessments.
Dr. John Hedengren is a Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 85 publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.