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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:Zheng\, Z. (STATS) - Semi-Supervised Statistical Learning for Oceanographic Data
DESCRIPTION:Oceanographic data\, generated by modern technologies that measure biological systems across time\, space\, and cell populations\, are often rich\, high-dimensional\, and highly heterogeneous. Such data provide valuable opportunities to study subcellular organization\, cellular heterogeneity\, and dynamic biological processes in marine environments. However\, because marine plankton systems remain relatively understudied and less well characterized than many model biological systems\, both data generation and labeling are particularly challenging. Limited domain knowledge and less mature laboratory protocols often produce noisy observations\, while reliable annotation requires substantial expert effort and is therefore difficult to obtain at scale.\nThis proposal develops statistical methodology for oceanographic data settings in which a small amount of expert-labeled data must be combined with a much larger collection of unlabeled or imperfectly processed data. A central goal is to incorporate limited scientific knowledge into statistical learning procedures to improve interpretability\, component identifiability\, and inferential reliability. In particular\, I develop semi-supervised statistical methods that explicitly quantify the information contributed by expert annotation.\nTo address this goal\, I study three related problems: semi-supervised functional clustering for subcellular spatial proteomics\, anchored semi-supervised mixture-of-experts models for flow cytometry\, and temporally structured latent-variable models that separate smooth trend and seasonal variation from scientific signals of interest. Together\, these projects aim to develop principled and interpretable methodology for partially labeled\, structured\, and high-dimensional oceanographic data\, with an emphasis on valid uncertainty quantification. \nEvent Host: Ziyue Zheng\, Ph.D. Student\, Statistical Science \nAdvisor: Sangwon Hyun \nZoom: https://ucsc.zoom.us/j/93229540289?pwd=8bsBOSBFmISlexmS4OWTmTZKp420u2.1
URL:https://events.ucsc.edu/event/zheng-z-stats-semi-supervised-statistical-learning-for-oceanographic-data/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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