Ross Gruetzemacher
PhD in Business (Management Information Systems; Business Analytics) Management Information Systems; Business Analytics Management Information Systems Business Analytics Artificial Intelligence Applications Artificial Intelligence in Education Applied science Computational and Text Analysis Methods Information Retrieval and Data Mining Information Systems and Technology Applications Economic Growth and Productivity Cognitive Science and Mapping Economic and Technological Systems Analysis Big Data Technologies and Applications Business, Education, Mathematics Research Knowledge Management in Higher Education Scenario planning Enterprise Management and Information Systems Accounting Artificial Intelligence Economics and Econometrics Education General Social Sciences History Information Systems Information Systems and Management Management of Technology and Innovation

Bio

Ross Gruetzemacher is a researcher affiliated with Wichita State University and Auburn University's Harbert College of Business. He holds a PhD in Business (Management Information Systems; Business Analytics), a Master of Science in Computational Engineering, and a Bachelor of Science in Mechanical Engineering. His research focuses on leveraging foundation models and deep transfer learning to enhance scientific research productivity and advance information systems. With 19 publications, 356 citations, an h-index of 8, and an i10-index of 7, his work demonstrates a growing impact in the fields of computing and business analytics.

Educational Journey

Auburn University

PhD in Business, Master of Science in Computational Engineering, Bachelor of Science in Mechanical Engineering • PhD in Business (Management Information Systems; Business Analytics)

Experience

0 - 0

0 - 0 • Harbert College of Business

Research

Leveraging Foundation Models for Scientific Research Productivity

Article January 23, 2026

he objective of this work was to elucidate paths for expediting and enhancing scientific research productivity from the emerging AI paradigm of foundation models (e.g., ChatGPT). Faster scientific progress can benefit mankind by speeding up progress toward solutions to shared human problems like cancer, aging, climate change, or water scarcity. Challenges to foundation model adoption in science threaten to slow progress in such research areas. This study attempted to survey decision support systems and expert system literature to provide insights regarding these challenges. We first reviewed extant literature on these topics to try to identify adoption patterns that would be useful for this purpose. However, this attempt, using a bibliometric approach and a very high level traditional literature review, was unsuccessful due to the overly broad scope of the study. We then surveyed the existing scientific software domain, finding there to be a huge breadth in what constitutes scientific software. However, we do glean some lessons from previous patterns of adoption of scientific software by simply looking at historical examples (e.g., the electronic spreadsheet).