Presenter: J. Xavier Prochaska, Professor, UC Santa Cruz Description: I will present two current and challenging research problems from the domains of astrophysics and oceanography which are leveraging standard Bayesian methods. After describing the datasets, scientific goals, and currently methodology, I will welcome a discussion on ways to improve, advance, and/or replace the current approaches. […]
Presenter: Dr. Kelli Gutierrez, CSU Monterey Bay Description: Many microswimmers propel themselves using flagella, which are thin, threadlike filaments that beat in a periodic wavelike motion. The flagellar beat emerges from the coupled interactions between the surrounding fluid and the active and passive responses of the flagellum. Previous studies have observed the qualitative shape of […]
Presenter: Enter the speaker’s name, title, and institution at the top of the event Description: Optimal transport (OT) provides a principled way to match, compare, and interpolate distributions, but its computational cost limits its use at realistic scales. I will present our recent work on reducing that cost and on using OT for statistical modeling […]
Presenter: Reza Fazel-Rezai, Senior Science and Education Application Engineer, MathWorks Description: Please join us to learn how to accelerate data analysis using a workflow that spans low-code tools, Generative AI, and Agentic AI. Through live demonstrations and practical examples, you’ll see how to import, explore, visualize, model, and process data using interactive tools that require […]
Presenter: Chrysoula Tsogka, Professor, Applied Mathematics, UC Merced Description: In a strongly scattering medium, waves undergo multiple scattering that is usually seen as an obstacle to imaging. Time-reversal experiments show the opposite: waves re-emitted into the medium refocus on their source more sharply than in a homogeneous medium, because scattering makes the array appear larger […]
Presenter: Edric Tam, Warren Alpert Fellow in AI and Computational Biology, Department of Biomedical Data Science, Stanford University Description: A longstanding question in Bayesian analysis concerns the role of randomization. We introduce randomization posteriors, a class of generalized Bayesian updates obtained by averaging over an auxiliary randomization law. A particularly relevant instance assigns random weights […]