Fall/Winter 2025 Issue
The online student membership magazine for INFORMS. The bi-annual publication provides a look at operations research and management science from the perspective of young people in those fields. Edited by a team of students and junior faculty, the magazine is written for students and aims to introduce topics relevant to them, highlight their accomplishments, and promote awareness of current events and issues in OR and MS.
Read The Letter From The Lead Editor Download PDF Version
We're always on a lookout for new members. Feel free to reach out to us at [email protected]
Hiring Call for Student Volunteers to be a part of the amazing student team that runs the magazine [Coming Soon!]
Mini Poster Competition 2026 [Coming Soon!]
Get creative and make your mark—don’t miss out on our mini poster competition. Exciting prizes await!
Thank you for the overwhelming response—2025 submissions are now closed.
Winners have been notified by email and will feature in our Fall 2025 December Issue.
For updates and the 2026 call, please follow our LinkedIn page.
Articles & Interviews
OR/MS Tomorrow Spotlights
The rise of Artificial Intelligence (AI) and powerful generative AI (genAI) models has sparked considerable excitement, promising to revolutionize fields from supply chain optimization to customer service automation. But beneath the hype, AI’s performance varies wildly. It excels in some tasks but can spectacularly fail in others that seem equally straightforward. This “jagged technological frontier” means that some tasks are easily handled by AI, while others, despite their apparent simplicity, remain beyond its current (and perhaps future) capabilities (Dell’Acqua et al., 2023).
Artificial Intelligence (AI) is a field of science focused on building systems capable of performing tasks that typically require human intelligence or on analyzing data at a scale far beyond human capacity. Artificial Intelligence (AI) is a field of science focused on building systems capable of performing tasks comparable to or exceeding human capabilities. AI has a plethora of applications in different domains, such as face recognition, medical diagnosis, playing chess, and language generation. In this article, we unpack some of the most common myths surrounding AI: what it can do, what it can’t, and what we often misunderstand. By clarifying these misconceptions, we hope to promote a more realistic and informed conversation about the future of these intelligent systems.
Generative AI is becoming more and more commonplace across every industries, yet the same model can deliver brilliant insights one moment and blatant errors the next. This article explores the sources of Gen AI unreliability and how concepts from reliability engineering can help make these systems more stable, transparent, and dependable.