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WSU Vancouver Mathematics and Statistics Seminar (Fall 2025)Welcome to the WSU Vancouver Seminar in Mathematics and Statistics! The Seminar meets on Wednesdays at 2:10–3:00 PM in VUB 124 (unless mentioned otherwise). This is the Undergraduate Building (marked "N" in the campus map). The seminar is open to the public, and here is some information for visitors. Students could sign up for Math 592 (titled Seminar in Analysis) for 1 credit. Talks will be given by external speakers, as well as by WSUV faculty and students. Contact the organizer Bala Krishnamoorthy if you want to invite a speaker, or to give a talk. Seminars from previous semesters |
| Date | Speaker | Topic | |
|---|---|---|---|
| Aug 20 | Organizational meeting | ||
| Aug 27 | Zach Fendler, WSU |
Adapting depth to Relocalization
Abstract (click to read)Relocalization is the task of estimating a camera's six degrees of freedom (6-DoF) pose within a known map, a problem with key applications in robotics, AR, and autonomous systems. In this talk, I will present our work from the G-RIPS Sendai 2025 program in collaboration with Mitsubishi Electric, where we developed a novel RGB-D relocalization pipeline designed to be robust to illumination changes. Our approach combines global feature extraction using ResNet18 + NetVLAD on both RGB and depth modalities with a late-fusion strategy (Net-AF) and local matching via ASIFT and SuperPoint. Depth data is further projected into 3D point clouds for pose estimation using RANSAC and the Kabsch algorithm. We evaluated our pipeline on the Stanford 2D-3D-Semantics dataset, including illumination-augmented variants, and observed strong performance under well-lit conditions, while identifying key challenges under low-light conditions. I will discuss the lessons learned, the promise of multimodal fusion, and future directions such as dynamic fusion weighting and depth-aware feature learning. |
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| Sep 3 | Elizabeth Thompson, WSU |
A Stable Measure of Chaos for Nonlinear Dynamical Systems Using Persistent Homology
Abstract (click to read)One widely used measure of chaos in nonlinear dynamical systems is the Lyapunov exponent, which measures the rate of exponential divergence of neighboring trajectories in a system's phase space. This measure relies solely on Euclidean distance, which we experimentally show decrease its stability on known chaotic systems when subject to small amounts of added Gaussian noise. Many real-world systems are arguably likely to contain some inherent noise. To address this limitation, we introduce a novel measure of chaos in nonlinear dynamical systems using persistent homology, the Persistence exponent, and prove its theoretical stability. We also show its greater experimental stability in comparison with the Lyapunov exponent on a variety of differential systems with known dynamics; modeling fluid motion, taffy pulling, and autocatalytic chemical reactions. We finally show greater experimental stability of the Persistence exponent on time series data obtained from a real Belousov-Zhabotinsky chemical reaction. |
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| Sep 10 | Elizabeth Thompson, WSU |
A Stable Measure of Chaos for Nonlinear Dynamical Systems Using Persistent Homology (continued)
Abstract (click to read)One widely used measure of chaos in nonlinear dynamical systems is the Lyapunov exponent, which measures the rate of exponential divergence of neighboring trajectories in a system's phase space. This measure relies solely on Euclidean distance, which we experimentally show decrease its stability on known chaotic systems when subject to small amounts of added Gaussian noise. Many real-world systems are arguably likely to contain some inherent noise. To address this limitation, we introduce a novel measure of chaos in nonlinear dynamical systems using persistent homology, the Persistence exponent, and prove its theoretical stability. We also show its greater experimental stability in comparison with the Lyapunov exponent on a variety of differential systems with known dynamics; modeling fluid motion, taffy pulling, and autocatalytic chemical reactions. We finally show greater experimental stability of the Persistence exponent on time series data obtained from a real Belousov-Zhabotinsky chemical reaction. |
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| Oct 8 | Jonathan McCollum, Oregon State U. | ||