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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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DTSTART;TZID=America/Los_Angeles:20260416T110000
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DTSTAMP:20260409T090136
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SUMMARY:Mirchandani\, C. (BMEB) - Population and Evolutionary Genomics Across Ecological Scales
DESCRIPTION:Sequencing technologies have transformed population and evolutionary genetics\, making it possible to ask questions at scales that were intractable a decade ago. Realizing that potential depends on tailored computational approaches\, and on the tools and infrastructure those approaches are built on. My dissertation works across this spectrum. Using an in vitro Drosophila cell culture system\, I show that mixed Wolbachia infections resolve rapidly and deterministically\, with one strain competitively excluding the other across host species and starting frequencies\, offering an explanation for why mixed infections are rarely observed in nature. In a deep-sea clam and its obligate bacterial symbiont\, I use two ultra-accurate sequencing methods and demographic modeling to directly estimate the effective transmission bottleneck between host generations\, finding it to be roughly eight symbionts\, orders of magnitude below prior cell-count estimates. I also present two tools for population genomics at scale: snpArcher\, a reproducible variant calling workflow developed for the California Conservation Genomics Project and now used across hundreds of species and tens of thousands of samples; and clam\, a Rust-based tool that efficiently estimates population genetic statistics by leveraging callable loci\, producing results identical to existing all-sites approaches at a fraction of the computational cost. Together\, these projects demonstrate how tailored computational approaches can unlock biological insight across diverse systems and scales. \nEvent Host: Cade Mirchandani\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics \nAdvisors: Russ Corbett-Detig & Shelbi Russell \nZoom- https://ucsc.zoom.us/j/98034081971?pwd=L5RoKoNEFxyapNhSRoXC8os2K2YZwv.1
URL:https://events.ucsc.edu/event/mirchandani-c-bmeb-population-and-evolutionary-genomics-across-ecological-scales/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260423T153000
DTEND;TZID=America/Los_Angeles:20260423T173000
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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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DTSTART;TZID=America/Los_Angeles:20260424T130000
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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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