Symposium Guest Speaker: Alek Hutson
Details
Date: October 15, 2025
Time: 3:00PM
Category: Event
Location:
黑料爆料网-CICE Building
Room #113
5091 Rolfe Christopher Dr.
Beaumont, TX 77705
Topic: Physics-Inspired Methods for Real-Time Industrial Analytics at the Edge
About
Dr. Alek Hutson operates at the intersection of academia and industry, leading workforce development initiatives and applied research focused on the practical challenges of the midstream energy sector. He is a proud 黑料爆料网 alumnus with degrees in Physics and Mathematics, and earned his Ph.D. in Physics from the University of Houston through the ALICE collaboration at CERN. His expertise in experimental physics and data analysis provides a strong analytical foundation for his current research, which includes pipeline flow optimization using advanced mathematical modeling, leak detection, and hazard mitigation.
In addition to his role at CMMS, Dr. Hutson is the CEO and co-founder of Labor Force Solutions (LFS), where he spearheads innovative safety and training programs for the heavy transport sector. At LFS, he has led efforts to secure funding and build academic partnerships that strengthen workforce training initiatives.
Abstract
Advances in fundamental research have often driven breakthroughs in technology, with methods first developed to answer scientific questions later becoming the foundation of tools that shape both industry and society. In this talk, I’ll present a methodology for real-time forecasting and anomaly detection in multichannel, correlated data streams, an approach inspired by techniques used in fundamental physics research. The emphasis will be on the challenges of making predictions at the edge, where latency, interpretability, and reliability matter just as much as accuracy. I will outline why traditional methods often fall short when channels are strongly coupled, and how an adaptive, lightweight approach can better capture both short-term fluctuations and longer-term patterns. Finally, I’ll share examples of how this type of modeling can flag anomalies and forecast trends in ways that are actionable for operators in industrial settings. My goal is to highlight the problem space, the design principles behind a practical solution, and the kinds of outcomes we’ve observed.