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Date: Saturday March 7th, 2026

Aproximate time: 8:30am - 3:30pm

The SIAM student chapters of Colorado are hosting their 22nd annual regional student conference on applied mathematics for all schools along the Front Range. This event will allow students from different universities in the area to see what is being done in this field and promote interest in applied mathematics in general. This event is open to both undergraduates and graduate students.

Poster

Program

All speakers and conference attendees should fill out a so we can keep you updated on any changes.

Location

This will be an in-person conference. Student speakers and attendees will attend in person on the CU Denver Auraria campus in the Student Commons Building. Please find the directions here.

Parking at CUDenver:

Parking at CUDenver: If you are planning to drive to the CUDenver campus there are several options on where to park. The closest parking options to the Student Commons building are hourly metered street parking, the Spruce Lot, and the Tivoli Parking Garage. It is recommended to park in the Spruce lot for uncovered parking and Tivoli Parking Garage for covered parking. There is a coupon code: 6029194, and to redeem the code and register to park follow this . This code can not be used at the north end of the 1st floor in the 7th Street Garage and the Juniper, Oak, and Maple Lots. Furthermore, this coupon code can NOT be used in metered spaces, service vehicle spaces, or loading zone spaces on campus.

Call for Presentations

All students (undergraduate and graduate) are invited to submit abstracts on any research topic in applied mathematics. Please submit your title and abstract as soon as possible, but no later than Friday, Feb 27 2026.ÌýAll titles and abstracts should be submitted toÌýFRAMSC.abstracts@gmail.com. For further direction on presentation format, see this page.

2026 SIAM Conference Keynote Speaker

Speaker

Dr. Joshua French

University of Colorado Denver

Title

Prefiltered Component-based Greedy (PreCoG) Scan Method

Abstract

The spatial distribution of disease cases can provide valuable insights into disease spread and risk factors. Identifying disease clusters correctly can lead to the discovery of new risk factors and inform interventions that can help control and prevent the spread of disease. In this regard, we propose a novel scan method, the Prefiltered Component-based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly-shaped clusters using a prefiltered component-based greedy search algorithm. The PreCoG scan method is computationally efficient, flexible in its ability to detect irregularly-shaped clusters, while still being powerful and having high levels of sensitivity and positive predictive value. To demonstrate its efficacy, we compare its performance to many other scan-based methods. Additionally, we have included this method in the smerc R package to make it easy to apply this method to new data sets. Our proposed PreCoG Scan Method offers a unique and innovative approach to cluster detection that can improve the efficiency and accuracy of disease surveillance systems.Ìý

About the Speaker

Conferences Photos

Previous SIAM Conferences