The Community Exchange
Letters to the editor, an E/A reflection, feedback from the quad and more
Winter 2026
“Letter to the Editor
I love the new look of The Exeter Bulletin. Particularly all the nice, crisp color photos in the class notes section. The articles are also wonderful. Keep up the good work. This is my first-ever comment to the editor of any magazine! I really enjoy the work you’re doing.”
Rob Pepper ’66
“E/A Weekend Reflection
“Exeter-Andover is my favorite part of the year. You can’t replicate the feeling of our volleyball team acing Andover, or the gleeful dizziness that comes from cheering just a touch too loud at the football game. You ignore the sticky feeling of face paint on your cheeks and the soreness in your legs from a game a few hours earlier, because in that moment, the only thing that matters is the energy surrounding you, and your voice in the sea of cheers that will propel game after game. ”

Morgan Signore ’26
Varsity girls soccer co-captain and co-head of the Big Red Zone
“I love the collaboration that Exeter is built on. But I also wanted the chance to take full ownership of a project. Independent work gave me the freedom to design and build a working prototype exactly how I imagined it. At a busy place like Exeter, having dedicated time to focus on a project I was passionate about was rare. I could experiment, iterate and actually bring my AI model to life. I loved the process of testing it, refining it and making it functional, all on my own schedule. Seeing an idea transform from concept to something real and usable was incredibly satisfying.”

Maya Shah ’26
Shah is one of seven Exonians who worked on independent study projects over the fall term. With support from Computer Science Instructor El Kaplan, Shah developed an AI-powered triage system to help emergency departments reduce overcrowding and long wait times. The system predicts patient urgency using symptoms, vital signs and medical history, assigning a 1–5 urgency score. Shah’s prototype includes a web-based interface for staff and an AI model trained on over 200,000 emergency department admissions from a dataset from Beth Israel Deaconess Medical Center in Boston.





