Empower Engineers in Analytics and Machine Learning with Education and Workforce Development

At the recent FOPAM conference, an insightful session shed light on the evolving landscape of The Past and Future of Process/Product Analytics & Machine Learning, including Education and Workforce Development. The session was chaired by Eva Sorensen (University College London) with participation from:

• Venkat Venkatasubramanian (Columbia University) on Combining Symbolic and Numeric AI: Challenges and Opportunities in Research and Education.
• John D. Hedengren (Brigham Young University) on Data-Driven Engineering Education with Hands-On Learning

with a concluding panel discussion that also included Warren D. Seider (Univ Pennsylvania).

The talk, titled "Data-Driven Engineering Education with Hands-On Learning," highlighted the crucial role of data engineering and machine learning in the era of Industry 4.0 and the imperative to equip emerging engineers with essential skills. The presentation underscored the significance of data engineering, encompassing vital aspects such as data acquisition, transport, curation, and storage, in driving modern industrial processes. It was noted that while various online repositories and example datasets exist, there is need for engineering-specific case studies.

To address this gap, a fresh array of resources was unveiled, aimed at fostering practical, hands-on data-driven engineering education. These resources are designed to impart aspiring engineers with vital proficiencies, including data collection, cleansing, pipeline establishment, storage methodologies, and strategies for quality assessment. Several hands-on activities were shared during the presentation, including methods for students and instructors to effectively integrate Large Language Models (LLMs) to enhance the learning process. Additional hands-on exercises included automotive data analysis and deep learning to detect human pose with form analysis.

The talk culminated in the announcement of an upcoming training initiative by AIChE, featuring several online courses through AIChE Academy, online (freely available for self-study), and live week-long courses in November and December 2023. Industry members gave feedback during the Q+A session that continuing education is a critical need to support individuals and organizations in the Industry 4.0 transition. The presentation emphasized the critical need for robust data engineering education and introduced a suite of valuable resources tailored to equip the next generation of engineers. As Industry 4.0 continues to redefine the industrial landscape, the importance of such educational initiatives becomes increasingly apparent.

The FOPAM 2023 conference, hosted by CACHE at the University of California, Davis demonstrates the continuous growth of this field. Following in the footsteps of its successful 2019 event, FOPAM provides a pivotal platform for experts from both academia and industry to engage in discussions concerning data analytics and machine learning in the process industries. The conference, co-sponsored by AIChE's CAST Division, AspenTech, AVEVA, Dow, Genentech, and the U.S. National Science Foundation, holds a growing place within this community.

Presentation on YouTube: https://youtu.be/sOQfCEmiYzM
Online Data-Driven Engineering Course: https://apmonitor.com/dde
Online Machine Learning for Engineers: https://apmonitor.com/pds