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SUMMARY:2026 Right Livelihood International Conference
DESCRIPTION:The Right Livelihood International Conference is a five-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/
CATEGORIES:Film Screening,Lectures & Presentations,Meetings & Conferences,Ph.D. Presentations,Seminars,Social Gathering,Training,Undergraduate,Workshop
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SUMMARY:Wang\, Q. (STAT) - Modern Statistical Methods for Modeling Spatial and Temporal Processes
DESCRIPTION:Modern scientific studies increasingly rely on complex datasets exhibiting spatial and temporal dependence\, particularly in social\, environmental\, and climate applications. This dissertation develops statistical models and computational methods for analyzing such data\, with an emphasis on capturing dependence structures\, nonlinear dynamics\, and uncertainty quantification. \nA spatial deep learning framework is developed to extend classical geostatistical models by incorporating convolutional neural network architectures\, allowing for flexible modeling of complex and nonstationary spatial dependence The proposed approach preserves principled uncertainty quantification alongside improved predictive performance for large and heterogeneous spatial datasets. \nIn the temporal domain\, a Bayesian hierarchical echo state network model is introduced for count-valued time series\, providing a flexible alternative to traditional autoregressive approaches. By embedding reservoir computing within a hierarchical probabilistic framework\, the model accommodates nonlinear temporal dynamics while enabling coherent inference and uncertainty quantification\, which are typically absent in standard neural network approaches. \nAlongside these model-driven developments\, we conduct a data-driven analysis of Northern Hemisphere snow cover using weekly satellite-derived observations from 1972 to 2024. A spatio-temporal modeling framework is developed that combines a seasonal two-state Markov structure for temporal dynamics with a Besag–York–Mollié (BYM) formulation to capture spatial dependence\, allowing both trend and seasonal effects to vary across space. Covariates including temperature\, latitude\, and elevation are incorporated to explain observed patterns. The analysis reveals substantial spatial heterogeneity and pronounced seasonal structure\, including week-specific trends and a coherent wave-like pattern of snow cover changes across continents. \nTogether\, this thesis addresses key limitations of classical approaches to spatial and temporal data analysis\, which often rely on restrictive assumptions that limit their ability to capture complex dependence structures and nonlinear dynamics. By integrating modern machine learning techniques with statistical modeling and complementing these developments with data-driven scientific analysis\, this dissertation provides a flexible and principled framework for understanding complex spatio-temporal processes while maintaining uncertainty quantification. \n  \nEvent Host: Qi Wang\, Ph.D. Candidate\, Statistical Science  \nAdvisor: Paul Parker \nZoom: https://ucsc.zoom.us/j/97486222296?pwd=419R7C5I6gLbbB0eLqwMcSVQLTN7bA.1 \nPasscode: 766602
URL:https://events.ucsc.edu/event/wang-q-stat-modern-statistical-methods-for-modeling-spatial-and-temporal-processes/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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