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SUMMARY:2026 Right Livelihood International Conference
DESCRIPTION:The Right Livelihood International Conference is a four-week global conference exploring how education can strengthen democracy\, collective intelligence\, and just futures. Bringing together Right Livelihood Laureates\, students\, faculty\, and community partners across continents\, the conference combines asynchronous learning with participatory dialogue and collaborative action. Rather than advocating specific outcomes\, the conference positions education as a democratic practice and the Right Livelihood College as a steward of dialogue\, student voice\, and long-term institutional learning. \nRegistration is free and open to the public. Sign up to receive conference updates\, session links\, and participation opportunities.
URL:https://events.ucsc.edu/event/2026-right-livelihood-international-conference/
LOCATION:
CATEGORIES:Film Screening,Lectures & Presentations,Meetings & Conferences,Ph.D. Presentations,Seminars,Social Gathering,Training,Undergraduate,Workshop
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SUMMARY:Pawl\, E. (STAT) - Flexible and Scalable Mixtures of Experts for Oceanographic Flow Cytometry Data
DESCRIPTION:Flow cytometry is a valuable technique in microbial research used to measure the optical properties of single-celled organisms at high throughput. Oceanographers often deploy flow cytometers on research cruises in order to study the characteristics of phytosynthetic microbes—called phytoplankton—in regions and times with diverse environmental conditions. Because cytometers cannot distinguish between subpopulations\, researchers typically cluster observations into subpopulations and subsequently analyze cluster characteristics. This two-stage workflow is often manual\, difficult to reproduce\, and fails to account for uncertainty in cluster assignments when relating subpopulation behavior to environmental conditions. To address these shortcomings\, statistical mixture models are gradually being introduced as alternatives to manual flow cytometry data analysis. However\, existing models either cannot use covariates or make restrictive assumptions about the relationships between cluster characteristics and covariates. Additionally\, they are designed to analyze individual cruises and consequently characterize local\, rather than global\, patterns in phytoplankton behavior. We propose to develop computationally efficient mixtures of experts which account for the complex dependency structures in oceanographic flow cytometry data. In this framework\, cells are probabilistically assigned to latent subpopulations\, while cluster-specific regressions relate each subpopulation’s optical properties and relative abundance to environmental conditions. Our first project develops a mixture of random weight neural network experts which can estimate arbitrary nonlinear regressions at low computational cost\, without a priori specification of functional forms. In the second project\, we develop a variational Bayesian mixture of experts which automatically selects variables without requiring cross-validation for hyperparameter selection. The final project incorporates spatial and temporal dependence\, allowing joint inference on data collected from multiple research cruises conducted at different locations and times. \nEvent Host: Ethan Pawl\, Ph.D. Student\, Statistical Science \nAdvisors: Sangwon Hyun & Paul Parker \nZoom- https://ucsc.zoom.us/j/96353239941?pwd=a4PJ94EMSD6D0SJ75S3WYzrPbYsBtn.1 \nPasscode- 244463
URL:https://events.ucsc.edu/event/pawl-e-stat-flexible-and-scalable-mixtures-of-experts-for-oceanographic-flow-cytometry-data/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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