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DTSTART;TZID=America/Los_Angeles:20260413T080000
DTEND;TZID=America/Los_Angeles:20260515T170000
DTSTAMP:20260513T225945Z
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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://live-events-ucsc.pantheonsite.io/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:20260420T100000
DTEND;TZID=America/Los_Angeles:20260420T110000
DTSTAMP:20260424T192938Z
CREATED:20260424T192938Z
LAST-MODIFIED:20260424T192938Z
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SUMMARY:Building Soil with Microbes: Compost as Biological Infrastructure
DESCRIPTION:Keisha Ernst from the Catalyst Bio-Amendments and Compost Academy\nIn Person Location: ISB 221 \nZoom Link \nIn this talk\, Keisha will explore how biologically focused compost production differs from conventional composting systems designed primarily for waste diversion. She will discuss how microbial communities influence soil structure\, nutrient cycling\, plant resilience\, and water dynamics—and how managing compost as a living biological input can shift the way we approach soil fertility. The presentation will also highlight findings from a two-year field trial evaluating microbial applications in landscape and turf systems\, including results showing measurable reductions in irrigation needs alongside improvements in soil performance. Designed for growers\, land managers\, researchers\, and soil enthusiasts alike\, this talk offers a practical look at how working with soil microbiology can reshape the future of agriculture and land stewardship. Keisha will also give a live demonstration showing different known types of microbiology!
URL:https://live-events-ucsc.pantheonsite.io/event/building-soil-with-microbes-compost-as-biological-infrastructure/
LOCATION:CA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T132500
DTEND;TZID=America/Los_Angeles:20260420T143000
DTSTAMP:20260424T192933Z
CREATED:20260424T192933Z
LAST-MODIFIED:20260424T192933Z
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SUMMARY: Waste and Cataclysm\, Waste as Catalyst: The Politics of Disposability in New Orleans
DESCRIPTION:Christopher Lang from the UCSC Environmental Studies Department\nIn Person Location: ISB 221 \nZoom Link \nLang explores the politics of disposability in New Orleans\, Louisiana\, revealing how pollution intersects with Black community health\, waste workers’ lives and livelihoods\, and the city’s overall resilience in the face of increasing flood risk. Using a combination of methods – from semi-structured interviews to formalized employment in city government to catch basin content analyses\, and more – Lang demonstrates that waste and its management in New Orleans is far from neutral\, neither in impact nor procedure; rather\, pollution that continually overwhelms the city stems from long-running political choices that reinscribe cultural norms and infrastructures around disposability\, which underpins the local and regional economy. Lang examines these consequences of mismanaged and excessive waste at different scales within the city\, noting several tensions that exist and impede structural improvements in sustainability and equity pertaining to solid waste: tensions between community and industry\, “cleanliness” and job security\, economy and the environment\, and tradition and adaptation. In a city that simultaneously experiences gentrification\, austerity\, and population loss (for myriad reasons: quality of life issues\, homeowner insurance costs\, climate change forecasting etc.)\, Lang unpacks the stakes of sustainability\, or lack thereof\, highlighting the power dynamics laden in New Orleans’ throwaway economy as well as those in efforts to “green” a whitening city.
URL:https://live-events-ucsc.pantheonsite.io/event/waste-and-cataclysm-waste-as-catalyst-the-politics-of-disposability-in-new-orleans/
LOCATION:CA
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260331T180549Z
CREATED:20260331T180549Z
LAST-MODIFIED:20260331T180549Z
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SUMMARY:AM Seminar: Variational Inference and Density Estimation with Non-Negative Tensor Train
DESCRIPTION:Presenter: Dr. Xun Tang\, Stanford University \nDescription: This talk covers an efficient numerical approach for compressing a high-dimensional discrete distribution function into a non-negative tensor train (NTT) format. The two settings we consider are variational inference and density estimation\, whereby one has access to either the unnormalized analytic formula of the distribution or the samples generated from the distribution. In particular\, the compression is done through a two-stage approach. In the first stage\, we use existing subroutines to encode the distribution function in a tensor train format. In the second stage\, we use an NTT ansatz to fit the obtained tensor train. For the NTT fitting procedure\, we use a log barrier term to ensure the positivity of each tensor component\, and then utilize a second-order alternating minimization scheme to accelerate convergence. In practice\, we observe that the proposed NTT fitting procedure exhibits drastically faster convergence than an alternative multiplicative update method that has been previously proposed. Through challenging numerical experiments\, we show that our approach can accurately compress target distribution functions. \nBio: Xun Tang is a postdoc in Stanford University\, department of mathematics\, hosted by Prof. Lexing Ying. Xun works on tensor network methods for scientific computing and data science\, and Xun also works on optimal transport algorithms. Xun will join HKUST department of mathematics in August 2026 as an incoming assistant professor. \nHosted by: Applied Mathematics Department
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-variational-inference-and-density-estimation-with-non-negative-tensor-train/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260331T181211Z
CREATED:20260331T181211Z
LAST-MODIFIED:20260331T181211Z
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SUMMARY:Statistics Seminar: Hierarchical Clustering with Confidence
DESCRIPTION:Presenter: Snigdha Panigrahi\, Associate Professor\, Department of Statistics\, University of Michigan \nDescription:Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However\, its greedy nature makes it highly sensitive to small perturbations in the data\, often producing different clustering results and making it difficult to separate genuine structure from spurious patterns. In this talk\, I will show how randomizing hierarchical clustering can be useful not just for measuring stability but also for designing valid hypothesis testing procedures based on the clustering results. We propose a simple randomization scheme to construct valid p-values at each node of a hierarchical clustering dendrogram\, quantifying evidence against greedy merges while controlling the Type I error rate. Our method applies to any linkage without case-specific derivations\, is substantially more powerful than existing selective inference approaches\, and provides an estimate of the number of clusters with a probabilistic guarantee on overestimation. \nBio:Snigdha Panigrahi is an Associate Professor of Statistics at the University of Michigan\, where she also holds a courtesy appointment in the Department of Biostatistics. She received her PhD in Statistics from Stanford University in 2018 and has been a faculty member at Michigan since then. Her research focuses on converting purely predictive machine learning algorithms into principled inferential methods. She is an elected member of the International Statistical Institute\, and her work has been recognized with an NSF CAREER Award and the Bernoulli New Researcher’s Award. Her editorial service\, past and present\, includes Journal of Computational and Graphical Statistics\, Bernoulli\, and Journal of the Royal Statistical Society: Series B. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-hierarchical-clustering-with-confidence/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260422T110000
DTEND;TZID=America/Los_Angeles:20260422T121500
DTSTAMP:20260401T165930Z
CREATED:20260331T171056Z
LAST-MODIFIED:20260401T165930Z
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SUMMARY:CSE Colloquium - Robust Machine Learning for Biomedical Data: Efficiency\, Reliability\, and Generalizability
DESCRIPTION:Presenter\nChenyu You\, Stony Brook University \nAbstract\nIn the rapidly growing area of machine learning\, there is profound promise in crafting intelligent\, data-driven methods for diverse real-world applications. Yet\, in safety-critical domains like healthcare\, some fundamental challenges remain: (1) The insufficiency of raw biomedical data emphasizes the need for data-efficient and robust learning approaches. (2) The imperative of safety and stability necessitates a cohesive framework that unifies learning with theoretical guarantees. (3) The inherent heterogeneity and distribution shifts in real-world clinical data call for robust and generalizable learning methods. To address these challenges\, there are several major directions I have explored: (i) (Robust) Machine Learning for Imperfect Medical Data: The development of machine learning models\, particularly in the context of label scarcity\, increasingly necessitates the collection of substantial annotated medical data. Moreover\, medical data often display a long-tailed class distribution\, which consequently results in notable imbalance issues. To this end\, there are several growing interests in training machine learning models jointly across imbalanced class distributions and limited annotations. I have developed novel\, efficient\, statistically consistent algorithms to improve empirical performance for biomedical image analysis. (ii) Learning with Theoretical Guarantees: As machine learning methods have become ubiquitous in clinical decision-making\, their reliability and interpretability have become important. This is particularly crucial in the field of biomedical image analysis\, where decision outcomes can have profound implications. I have developed novel machine learning algorithms that enable provably accurate anatomical modeling with theoretical guarantees. (iii) Generalize across Diverse Biomedical Data: The development of medical foundation models often requires massive and diverse biomedical data. To this end\, I have developed various foundation models for biomedical imaging data and explored novel applications of these models. I have also developed novel medical AI Agents that lead to the scalable and accurate predictive modeling\, particularly for distribution shift problems. \nSpeaker Bio\nChenyu You is an Assistant Professor in the Department of Applied Mathematics & Statistics and Department of Computer Science at Stony Brook University. He is also the core faculty member of the CVLab\, AI institute\, and affiliated with the Institute for Advanced Computational Science. His research focuses on both fundamental and applied problems in computer vision and machine learning\, often with a focus on generalization\, and making machine learning more reliable. Our applied research includes applications to healthcare\, biomedical imaging\, and cognitive neuroscience. He received his Ph.D. in 2024 from Yale University under the advisement of James S. Duncan\, his M.S. in 2019 from Stanford University under the advisement of Daniel Rubin\, and his B.S. in 2017 from Rensselaer Polytechnic Institute under the advisement of Ge Wang\, all in electrical engineering. He has also spent wonderful time at Facebook AI Research (FAIR)\, as well as Google Research. He serves on the Medical Image Computing and Computer-Assisted Intervention Society (MICCAI)\, and the SUNY AI Symposium Planning Committee\, and as associate editors for IEEE Transactions on Medical Imaging\, Medical Image Analysis\, IEEE Transactions on Neural Networks and Learning Systems\, Pattern Recognition\, and Transactions on Machine Learning Research. He has received AAAI’26 New Faculty Highlights\, CPAL’26 Rising Stars Award\, Tinker Research Grant Award\, Lambda Research Grant Award\, ICML’25 Oral Presentation Award\, EMBC’25 Top Paper Award\, MICCAI’25 NIH Registration Grant Award\, IEEE TMI’25 Distinguished Associate Editor Certificate of Excellence Award\, and Yale George P. O’Leary Graduate Fellowship\, and has been ranked as the World’s Top 2% most-cited scientists by Stanford University since 2024\, is a member of the Sigma Xi scientific research society\, and received the Excellence in Teaching Award for Spring and Fall 2025. For more information\, please check his website: https://chenyuyou.me/. \nHosted by: Professor Yuyin Zhou \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://live-events-ucsc.pantheonsite.io/event/cse-colloquium-robust-machine-learning-for-biomedical-data-efficiency-reliability-and-generalizability/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260423T114000
DTEND;TZID=America/Los_Angeles:20260423T131500
DTSTAMP:20260423T163021Z
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SUMMARY:BME 280B Seminar: Speaker Dr. Aaron Newman - Molecular and spatial determinants of single-cell developmental states in cancer
DESCRIPTION:Presenter: Dr. Newman\, Associate Professor in the Department of Biomedical Data Science\, Stanford University \n  \nDescription: Determining the factors that shape cell potency—the ability of a cell to differentiate into other cell types—is essential for understanding tissue biology in health and disease\, including cancer. In previous work\, we found that single-cell transcriptional diversity decreases across developmental time\, from the fertilized egg to the most mature cells in the body\, and in multiple organisms. More recently\, we developed CytoTRACE 2\, an interpretable AI framework trained on millions of data points from single-cell RNA sequencing data\, to determine cell potency on an absolute scale and reveal molecular hallmarks of developmental potential. We are now leveraging this method along with advances in spatial transcriptomics\, to identify multicellular ecosystems linked to cancer cell differentiation states and clinical outcomes. I will highlight these tools along with our ongoing work to decode cell plasticity and clinically relevant spatial microenvironments in human malignancies. \n  \nBio: Dr. Newman is an Associate Professor in the Department of Biomedical Data Science at Stanford University and a Chan Zuckerberg Biohub Investigator. He is also a member of the Stanford Cancer Institute and the Stanford Institute for Stem Cell Biology and Regenerative Medicine. Dr. Newman has made significant contributions to computational biology with applications to liquid biopsy\, cancer genomics\, and tumor immunology. Key contributions include CAPP-Seq for ultrasensitive detection of circulating tumor DNA; CIBERSORT/x for decoding cellular composition from bulk genomic data; CytoTRACE/2 for inferring cellular differentiation states from scRNA-seq data; and EcoTyper for delineating context-dependent cellular ecosystems from bulk\, single-cell\, and spatial expression data. His research program focuses on developing innovative data science tools to study the phenotypic diversity\, differentiation hierarchies\, and clinical significance of tumor cells and their surrounding microenvironments. Key results are further explored experimentally\, both in the lab and through collaboration\, with the goal of translating promising findings into the clinic.  \nHosted by: Professor Camilla Forsberg\, BME Department
URL:https://live-events-ucsc.pantheonsite.io/event/molecular-and-spatial-determinants-of-single-cell-developmental-states-in-cancer/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260424T132000
DTEND;TZID=America/Los_Angeles:20260424T142500
DTSTAMP:20260422T224826Z
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SUMMARY:BME80G Seminar: Ed Green\, "DNA Forensics in The Genomics Age"
DESCRIPTION:Presenter: Richard “Ed” Green\, Professor of Bimolecular Engineering @ UCSC \nBio: Richard E. Green (Ed) was born in Atlanta\, Georgia\, USA in 1972. He graduated from the University of Georgia (B.Sc. Genetics) in 1997. Before graduate school\, Ed was in Peace Corps (Barentu\, Eritrea) and was a lab tech at Emory University. Ed studied with Steven Brenner at the University of California\, Berkeley where he got his PhD in 2005 on computational algorithms for sequence analysis and alternative splicing. As an NSF Postdoctoral Fellow in the lab of Svante Paabo at the Max Planck Institute for Evolutionary Anthropology\, Ed pioneered the use of high throughput sequencing in ancient DNA. He was first author of the paper in Science describing the Neanderthal genome which won the Newcombe-Cleveland prize. As Professor at the University of California\, Santa Cruz\, Ed co-directs the UCSC Paleogenomics lab. His research focuses on comparative genomics\, population genetics\, DNA technology development\, and DNA-based forensics. Ed is co-founder of Dovetail Genomics\, Claret Biosciences\, and Astrea Forensics. He is a Kavli Scholar\, a Searle Scholar and a Sloan Scholar\, author of over 100 research manuscripts and 21 US Patents. He is a senior member of the National Academy of Inventors\, was a 2024 Santa Cruz Titan of Tech\, and was awarded the 2025 International Homicide Investigators Association technology award. \n\nHosted by: Professor Karen Miga\, BME Department
URL:https://live-events-ucsc.pantheonsite.io/event/bme80g-seminar-ed-green-dna-forensics-in-the-genomics-age/
LOCATION:Jack Baskin Auditorium\, 191 Baskin Cir\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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