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DTSTART;TZID=America/Los_Angeles:20250813T100000
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DTSTAMP:20260421T080052
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SUMMARY:Interested in a paralegal career?
DESCRIPTION:You are invited to join a free\, online informational session to learn more about the Center for Legal Studies Paralegal Certificate Course©\, a professional education program taught through the UCSC Silicon Valley Extension Paralegal Studies program. \nTopics \n\n\nCareer growth and earning potential \n\n\nAn overview of how CLS works with accredited college and university partners \n\n\nCourse pricing and payment options \n\n\nCourse materials and textbooks \n\n\nInstructor and peer/student interactions \n\n\nHow the online course works \n\n\n\nQ&A: There will be 15 minutes dedicated to Q&A\, so please bring any questions you might have.  \nRegister to receive the event recording \nIf you are unable to attend the webinar at this date and time\, please still register and the BARBRI team will send out the recorded webinar for you to view. \nThis event is co-sponsored by the UCSC Silicon Valley Professional Education Legal Studies program.
URL:https://events.ucsc.edu/event/interested-in-a-paralegal-career-8312/
CATEGORIES:Lectures & Presentations,Training
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DTSTART;TZID=America/Los_Angeles:20250813T100000
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DTSTAMP:20260421T080052
CREATED:20250806T070000Z
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SUMMARY:Asefi\, N. (ECE) - Generative Lagrangian Data Assimilation for Ocean Dynamics under Extreme Sparsity
DESCRIPTION:Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse\, irregular\, and Lagrangian nature of spatial sampling\, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural operators with denoising diffusion probabilistic models (DDPMs) to reconstruct high-resolution ocean states from extremely sparse Lagrangian observations. By conditioning the generative model on neural operator outputs\, the framework accurately captures small-scale\, high-wavenumber dynamics even at $99%$ sparsity (for synthetic data) and $99.9%$ sparsity (for real satellite observations). We validate our method on benchmark systems\, synthetic float observations\, and real satellite data\, demonstrating robust performance under severe spatial sampling limitations as compared to other deep learning baselines. \nEvent Host: Niloofar Asefi\, PhD Student\, Electrical & Computer Engineering \nAdvisor: Ashesh Chattopadhyay
URL:https://events.ucsc.edu/event/asefi-n-ece-generative-lagrangian-data-assimilation-for-ocean-dynamics-under-extreme-sparsity/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
GEO:37.0009723;-122.0632371
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DTSTART;TZID=America/Los_Angeles:20250813T120000
DTEND;TZID=America/Los_Angeles:20250813T120000
DTSTAMP:20260421T080052
CREATED:20250924T212032Z
LAST-MODIFIED:20250924T212032Z
UID:10000047-1755086400-1755086400@events.ucsc.edu
SUMMARY:Lunch & Learn: Onboarding Graduate Students for Research
DESCRIPTION:Join colleagues from the Office of Research Compliance Administration and Baskin Engineering on August 13\, 12-1 p.m.\, for a practical overview of onboarding graduate students in their role as researchers. This session will cover key institutional requirements needed before students begin research activities. Speakers will highlight common pitfalls\, share best practices\, and offer strategies to set students up for success.
URL:https://events.ucsc.edu/event/lunch-learn-onboarding-graduate-students-for-research/
CATEGORIES:Training
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