A Data-Driven Analysis for Engineering Conferences: The Institute of Industrial and Systems Engineering (IISE) Annual Conference Proceedings (2002-2025)

Researchers H. Sinan Bank and Casey E. Eaton have published a novel computational analysis that charts the intellectual history of Industrial and Systems Engineering (ISE) using AI. Their paper, 'A Data-Driven Analysis for Engineering Conferences,' leverages Large Language Models (LLMs) for domain-aware classification, Natural Language Processing (NLP), and Network Science to systematically analyze 24 years of IISE Annual Conference proceedings. This approach overcomes the limitations of traditional literature reviews by processing the massive scale of scholarship—an initial dataset of 9,350 paper titles for thematic analysis and 8,958 for citation tracking—to reveal the field's thematic shifts and complex collaboration networks over two decades.

The study provides a comprehensive 'cartography' of the ISE field, identifying which research topics have been dominant, which are emerging, and which are receding. By analyzing citation data and co-authorship networks, it also uncovers influential papers and authors, offering critical insights into knowledge diffusion and community structure. The researchers will make their curated dataset publicly available to foster reproducibility. This work establishes a data-informed baseline for understanding the trajectory of ISE research, offering a powerful new tool for researchers, practitioners, and educators to guide the field's future direction based on empirical evidence of its past.