The Fall 2025 meeting will take place at Taylor University on Saturday, October 18, 2025. Check-in begins at 8:30 AM in the Euler Science Complex, and the welcome address will begin at 9:30 AM.
The local organizer is Dr. Derek Thompson (drthompson@taylor.edu).
Hotel Accommodations
Holiday Inn Express - Gas City
4914 North Beaner Boulevard, Gas City, Indiana, 46933
Date: October 17-18, 2025 / 1 night
Front desk: 765-674-6664
To reserve a room under the group block, call the reservations line 888-465-4329 by September 26, 2025. Provide the group name “IN-MAA Conference” and group code EG7 (1 King Standard) or EG8 (2 Queens Standard). Or, book using the following links: 1 King Standard, 2 Queens Standard.
Talks / Posters
If you are planning to give a contributed talk or present a poster, please submit the Call for Papers form. The submission deadline is October 4, 2025.
Plenary Speakers
AI Essentials: Getting Up to Speed for Informed Decisions
Professor of Mathematics
Director of the Center for Learning and Teaching
Denison University
This plenary will have a seminar component for those interested in gaining some hands-on practice with the tools discussed in the presentation. More details to follow.
Self-Interacting Random Walks
Professor of Mathematics
Purdue University
In the classical model for a simple one-dimensional random walk, a random walker steps right or left according to a sequence of independent coin flips. While this model is mathematically tractable and has found many applications in diverse fields such as physics, chemistry, economics, computer science and more, there are some situations where one would like to model random motion that has some more complicated dependence structure. In this talk I will introduce a few different models of self-interacting random walks that I have studied and show how these models can produce interesting (and surprising) asymptotic behavior that is sometimes very different from that of a classical random walk (e.g. non-Gaussian limiting distributions). These random walk models are often simple enough for an undergraduate student to understand (and I will present some results that undergraduate students have obtained in REUs I have directed) but also generate deceptively difficult questions that continue to challenge even experts in the field.