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DTSTART;TZID=America/Los_Angeles:20260423T150000
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SUMMARY:High-Performance Mesh Generation for Scientific Computing and Graphics
DESCRIPTION:This workshop introduces core methods for generating high-quality meshes used in large-scale simulation\, scientific computing\, and computer graphics. We will explore how Voronoi tessellation and Delaunay triangulation form the foundation of modern meshing algorithms\, how parallel computing enables scalable geometry processing\, and how emerging learning-based approaches can learn to generate meshes directly from data. \nThe session will demonstrate several open-source tools\, including: \n\nVoro++ — a multi-threaded library for scalable Voronoi diagram computation\,\nTriMe++ — a high-performance library for fast mesh generation\, and\nVoroLight — a lightweight learning-based framework for producing Voronoi meshes from general inputs.\n\nParticipants will gain both theoretical insight and practical experience with modern meshing pipelines. \n\nThis workshop will be hosted by IDRE Fellow\, Dr. Jiayin Lu. \nRegister Now!
URL:https://workshops.ucla.edu/workshop/high-performance-mesh-generation-for-scientific-computing-and-graphics/
LOCATION:Zoom
CATEGORIES:Training workshop / Tutorial
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DTSTART;TZID=America/Los_Angeles:20260325T100000
DTEND;TZID=America/Los_Angeles:20260325T120000
DTSTAMP:20260619T170917
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LAST-MODIFIED:20260324T003032Z
UID:10003704-1774432800-1774440000@workshops.ucla.edu
SUMMARY:Physic-informed diffusion models for medical imaging
DESCRIPTION:This workshop will introduce the UCLA research community to the application of diffusion models for medical imaging problems\, with a focus on MRI. Participants will learn both the fundamentals of diffusion and how it can be adapted to physics-constrained scenarios\, such as k-space undersampling in MRI. \nTarget Audience: Graduate students\, postdocs\, and faculty in computational sciences\, biomedical physics\, computer science\, and engineering. Imaging scientists and clinicians interested in machine learning for medical image reconstruction. Any researchers in other fields (astronomy\, microscopy\, geoscience) where inverse problems and undersampled acquisitions are common. \nLearning Outcomes: \n\nUnderstand the fundamentals of diffusion models.\nUnderstand the basics of MRI reconstruction and how it is treated as an inverse problem.\nGain insight into how k-space undersampling can be formulated as a “forward process” for cold diffusion.\nLearn about tools for implementing custom forward processes in PyTorch.\nExplore how measurement conditioning integrates physical constraints with learned priors.\nDiscuss broader applications of physics-informed diffusion models across other scientific imaging domains.\n\n\nThis workshop will be hosted by IDRE Fellow\, Dr. Thomas Coudert. \nRegister Now!
URL:https://workshops.ucla.edu/workshop/physic-informed-diffusion-models-for-medical-imaging/
LOCATION:Zoom
CATEGORIES:Training workshop / Tutorial
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