Graduate Student Seminars
Sep 24, 2025 03:00 PM
354 Parker Hall
Speaker: Dr. Luke Oeding (Auburn University)
Abstract: Entanglement is a resource that is crucial for the so-called quantum advantage promised by quantum computing. How can algebra help us to understand quantum entanglement? I’ll explain our recent work that combines algebra and optimization to find new instances of maximally entangled states.
This is based on joint work with my recent PhD student, Ian Tan, who’s now working as a Postdoc at Charles University in Prague.
Sep 17, 2025 03:00 PM
354 Parker Hall

DMS Graduate Student Seminar
Sep 10, 2025 03:00 PM
Parker Hall 354
Speaker: Dr. James Scott (Auburn University)
Title: New Horizons in Nonlocal Modeling
Abstract: In recent years, continuum models that incorporate nonlocal effects have seen greatly increased use. They have been applied in many areas, including diffusion modeling, image processing, and mechanics. These models are characterized by partial integro-differential equations; that is, in place of partial derivatives, integral operators that act on difference quotients of multi-variable functions are used. In this talk, we will introduce some of these nonlocal models and discuss recent contributions to their mathematically rigorous underpinning across several different contexts. Such contributions include the well-posedness and regularity of nonlocal equations, the robust nature of their discretizations, rigorous characterizations of long-range and other phenomena captured by the equations, and the consistency of nonlocal models with classical models in suitable asymptotic regimes. The contexts include continuum mechanics, semi-supervised learning, fractional PDEs, and coupled local-nonlocal equations. We will conclude with a presentation of some unanswered questions.
DMS Graduate Student Seminar
Sep 03, 2025 03:00 PM
354 Parker Hall

DMS Graduate Student Seminar
Aug 27, 2025 03:00 PM
354 Parker Hall

Title: More of Less: A Rashomon Algorithm for Sparse Model Sets
Abstract: The current paradigm of machine learning consists in finding a single best model to deliver predictions and, if possible, interpretations for a specific problem. This paradigm has however been strongly challenged in recent years through the study of the “Rashomon Effect” which was coined initially by Leo Breiman. This phenomenon occurs when there exist many good predictive models for a given dataset/problem, with considerable practical implications in terms of interpretation, usability, variable importance, replicability and many others. The set of models (within a specific class of functions) which respect this definition is referred to as the “Rashomon set” and an important amount of recent work has been focused on ways of finding these sets as well as studying their properties. Developed in parallel to current research on the Rashomon Effect and motivated by sparse latent representations for high-dimensional problems, we present a heuristic procedure that aims to find sets of sparse models with good predictive power through a greedy forward-search that explores the low-dimensional variable space. Throughout this algorithm, good low-dimensional models identified from the previous steps are used to build models with more variables in the following steps. While preserving almost-equal performance with respect to a single reference model in a given class (i.e., a Rashomon set), the sparse model sets from this algorithm include diverse models which can be combined into networks that deliver additional layers of interpretation and new insights into how variable combinations can explain the Rashomon Effect.
DMS Graduate Student Seminar
Apr 23, 2025 03:00 PM
ACLC 010

DMS Graduate Student Seminar
Apr 02, 2025 03:00 PM
010 ACLC
Speakers: Melinda Lanius and Elvan Ceyhan (Auburn University)
Title: Mute your mic if you need to cry! Exploring qualitative approaches to data analysis through a study of undergraduates' emergency remote learning experiences.
Abstract: Mathematics Discipline-Based Education Research (Math-DBER) is an interdisciplinary research area that utilizes the fields of Mathematics, Data Science, and Education to investigate the teaching and learning of mathematics. In this interactive talk, we will explore approaches to qualitative data analysis through a study of undergraduates' emergency remote learning experiences. We will employ a by-hand technique called qualitative coding (an education researcher's approach) to a data set and then compare our results to a text analysis (data scientist's approach).
DMS Graduate Student Seminar
Mar 26, 2025 03:00 PM
010 ACLC

Abstract: Let \(G\) be a graph. An edge coloring of \(G\) is an assignment of colors to the edges of \(G\) such that no two adjacent edges share the same color. In this talk, we will explore problems and conjectures related to edge colorings of graphs, as well as several conjectures concerning variations of edge colorings.
DMS Graduate Student Seminar
Nov 20, 2024 03:00 PM
010 ACLC

DMS Graduate Student Seminar
Nov 13, 2024 03:00 PM
010 ACLC
Dr. Melinda Lanius will lead a panel discussion on Teaching & Scholarship at Teaching-Focused Institutions.
The following are the panelists.
DMS Graduate Student Seminar
Nov 06, 2024 03:00 PM
010 ACLC
