UNCW Data Science Seminar

The Data Science/AI Seminar is interdisciplinary. All faculty and students are welcome. Email DataScienceUG@uncw.edu if you are interested in presenting or would like to join the seminar mailing list.

Note: The time and location may vary.

Fall 2026 Schedule

Abstract

Bilipschitz embeddings for invariant machine learning - D. Mixon

Machine learning algorithms are typically designed for Euclidean data, but many natural datasets come with symmetries: a group G of isometries acts on a Euclidean space V, and points in the same orbit represent the same object. That means the true data space is not V, but the orbit space V/G. Invariant machine learning represents this quotient by a G-invariant feature map into Euclidean space. For robustness, especially against adversarial examples, this feature map should be bilipschitz with respect to the quotient metric. Sadly, vanilla polynomial invariants fail to be bilipschitz, so we need to move beyond classical invariant theory. In this talk, we present low-distortion embeddings in a variety of settings, and we conclude with applications and several open problems.

AI, Your Career, and the Skills That Still Matter - B. Stensvad

UNCW is pleased to welcome Bryan Stensvad for a guest talk on applied AI in industry. Bryan leads advanced analytics and AI initiatives that directly move the needle for a global technology company — from dynamic pricing recommendation engines and sales productivity platforms to fraud detection systems — work that has driven measurable revenue growth, margin expansion, and operational efficiency at scale. With a career spanning strategy consulting, corporate strategy, and hands-on AI implementation, Bryan brings a rare combination of technical depth and business acumen, offering students a firsthand look at how AI translates into real bottom-line impact inside a Fortune Global 500 company.

40-minute talk + 20-minute open Q&A
Ask Bryan directly about career strategy, industry trends, and breaking into AI-driven roles

Unveiling the Invisible: PM2.5 at NC Schools & Its Academic Shadows - R. Carroll

Fine particulate matter (PM2.5) is a major environmental health concern, particularly for children, yet estimating exposure at school locations remains challenging due to limited monitoring coverage. This study estimates PM2.5 concentrations at North Carolina public schools by combining high-quality but sparse EPA monitoring data with dense, low-cost PurpleAir sensor data. Two interpolation approaches—Kriging and inverse distance weighting (IDW)—were compared using leave-one-out cross-validation. Results show that Kriging consistently outperformed IDW, and that incorporating PurpleAir data improved predictive accuracy, especially in areas lacking EPA monitors. School-level estimates revealed higher PM2.5 concentrations in suburban areas relative to urban and rural settings. Linking these estimates to educational outcomes, higher PM2.5 levels were associated with lower 8th-grade reading scores, though effects were modest. Overall, the study demonstrates that integrating low-cost sensors with traditional networks enhances air pollution exposure assessment and suggests potential implications of air quality for student academic performance.

Leveraging Satellite Data and Geospatial AI for Agricultural Monitoring and Decision Support - Z. Yang

Satellite remote sensing provides repeated observations of ecosystem dynamics across different wavelengths and at various spatial and temporal resolutions. The increasing availability and capability of satellite observations have created new opportunities for remotely monitoring agricultural systems across broad spatial scales throughout the growing season. In this talk, we focus on how Geospatial AI can help integrate multi-source satellite data with environmental observations to enable timely agricultural monitoring under varying growing conditions. Specifically, we discuss a variety of applications ranging from in-season crop type classification, phenology monitoring, to yield prediction. Building on these applications, we are developing integrated approaches to assess flooding impacts on agriculture in the Inner Coastal Plain of North Carolina, with the broader goal of providing decision support for agricultural resilience planning.