معرفی
Eric Brown is an Assistant Professor of Practice and Director of the Undergraduate Program in the Department of Information Systems and Quantitative Sciences at the Rawls College of Business, Texas Tech University. His academic roles include teaching undergraduate and graduate courses in data science, information systems, and operations management.
Dr. Brown holds a PhD in Industrial Engineering from Texas A&M University (2020), where his dissertation focused on efficient algorithms for contingency response in electric power systems. He also earned an MEng in Industrial Engineering from Texas A&M (2011) and a BS in Mathematics from Abilene Christian University (2008).
His research interests are centered on the intersection of optimization and machine learning, with applications in power systems and medical treatment. Specifically, he focuses on:
- Data mining theory, particularly imbalanced data classification and medical applications
- Mathematical modeling of linear and nonlinear methods in power systems optimization
- Development of algorithms for discrete nonlinear optimization
His recent publications (2018-2023) emphasize advancements in power systems optimization and medical data science. Key contributions include developing heuristic methods for post-contingency power flow violations and undersampling techniques for imbalanced medical datasets.
He has received the DHS HS-STEM Career Development Grant Fellowship (2010-2011), supporting his research in Homeland Security-related fields.
In teaching, he contributes to courses in data science and operations research. His research has been supported by grants such as the DHS fellowship. He collaborates with institutions like the National Security Agency and Teledyne Brown Engineering through past professional roles.
