Note on graph on the left (courtesy of Dr. aBa Mbirika). Source:  aBa Mbirika and Paige Elizabeth Simanski (UWEC student), 2026, Research work on "A decomposition of the fundamental period of the Lucas sequence". The drawing is a visual representation of the Lucas sequence taken modulo 16. This sequence repeats every 16 terms, and interestingly enough, each least residue class modulo 16 appears exactly once in a particularly pleasing manner. 

Full Program [ pdf   ]

Short Program [ pdf   ]

Morning Cookies and Coffee                                                                                                                                                            Hibbard 223

7:50 - 9:00

Get some cookies to start your day!

Talks                                                                                                                                                                                                                                 Hibbard 203, 231, 302, 312, 320, and 323

8:30-12:30

Come and take part in great talks by students and faculty!

Lunch                                                                                                                                                                                                                           Hibbard 102

12:30-1:30

Please stop by to meet the speakers and share some food.

Andrew Balas Lecture

Hibbard 102

2:00-2:50

Statistical Methods for Multi-Stage Optimal Decision-Making                                                                                                      Hibbard 102

Dr. Abdus S. Wahed, University of Rochester                                                                                                                                                   

Abstract: Decisions made at a given stage of a process can constrain or enable future actions, thereby influencing long-term outcomes. In many scientific domains, such as precision medicine, public policy, and economics, the quality of an initial decision cannot be evaluated solely by its immediate effect, but rather by its consequences across an entire sequence of future decision points. For example, an initially modestly effective chemotherapy option may lead to improved long-term survival when followed by an appropriate salvage regimen. This motivates statistical methods that explicitly account for downstream interventions, evolving covariate processes, and future decision rules. Within the framework of dynamic treatment regimes and reinforcement learning, estimation of optimal sequential decisions requires modeling both immediate and future conditional gains or rewards. In this talk, we will discuss Q-learning as a statistical learning approach for estimating optimal dynamic treatment regimes. I will emphasize its interpretation, implementation, and theoretical properties, as well as its strengths and limitations relative to alternative methods. The goal is to illustrate how forward-looking statistical decision strategies can yield improved long-term outcomes.

Mathematics Competition                                                                                                                                 

Hibbard 102

3:30-4:30

The 34th Annual Math Retreat will conclude with the Mathematics Competition.  Join our students as they compete to solve a series of challenging Mathematics problems.

All Abstracts [  pdf  ]