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DTSTART;TZID=America/Los_Angeles:20260423T150000
DTEND;TZID=America/Los_Angeles:20260423T170000
DTSTAMP:20260505T113330
CREATED:20251219T193409Z
LAST-MODIFIED:20260423T014213Z
UID:10003705-1776956400-1776963600@workshops.ucla.edu
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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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260325T100000
DTEND;TZID=America/Los_Angeles:20260325T120000
DTSTAMP:20260505T113330
CREATED:20251219T193409Z
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T140000
DTEND;TZID=America/Los_Angeles:20260309T160000
DTSTAMP:20260505T113330
CREATED:20251219T193409Z
LAST-MODIFIED:20260309T001523Z
UID:10003703-1773064800-1773072000@workshops.ucla.edu
SUMMARY:AI for Biomedical Microscopy Data Reconstruction
DESCRIPTION:This workshop will explore how artificial intelligence is used to reconstruct biomedical microscopy data\, emphasizing both the opportunities and the risks of these approaches. While AI has enabled remarkable advances in virtual staining\, super-resolution\, and image translation\, biomedical imaging presents unique challenges that make hallucinations far more consequential than in other domains. A false structure in a pathology slide is not just an artifact\, it can represent a disease feature or even a “patient” that never existed. The workshop will therefore focus on the nuances of model selection\, data preprocessing\, and evaluation strategies needed to responsibly apply AI in biomedical microscopy. \nTarget Audience: This workshop is designed for computational scientists collaborating with biomedical researchers who want deeper insight into the challenges of high‑stakes medical imaging. It is also open to undergraduate and graduate students\, as well as other biomedical researchers. \nLearning outcomes: \n— Identify risks of AI hallucinations in biomedical imaging and explain why these differ from other application areas. \n— Compare the performance characteristics (strengths and weaknesses) of GANs\, CycleGANs\, and diffusion models in microscopy reconstruction. \n— Evaluate the role of data cleaning\, preprocessing\, and representation in reducing model failure. \n— Apply criteria to assess whether an AI-generated biomedical image is trustworthy for downstream interpretation. \n— Differentiate between appropriate and inappropriate use cases of AI in microscopy based on model behavior and validation evidence. \n\n\nThis workshop will be hosted by IDRE Fellow\, Dr. Paloma Casteleiro Costa. \nRegister Now!
URL:https://workshops.ucla.edu/workshop/ai-for-biomedical-microscopy-data-reconstruction/
LOCATION:OARC Portal\, Math Sciences 5628\, 5628 Math Science Building\, UCLA
CATEGORIES:Training workshop / Tutorial
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T100000
DTEND;TZID=America/Los_Angeles:20260209T120000
DTSTAMP:20260505T113330
CREATED:20251219T193408Z
LAST-MODIFIED:20260209T231253Z
UID:10003702-1770631200-1770638400@workshops.ucla.edu
SUMMARY:Machine Learning for Atomic-Scale Materials Modeling and Simulation
DESCRIPTION:This workshop will introduce researchers to cutting-edge machine learning (ML) methods that are transforming computational chemistry and materials science. Participants will learn how ML models can accelerate atomistic simulations\, bridging the gap between quantum-level accuracy and realistic system sizes. The session will highlight practical workflows\, including training potentials on quantum mechanical data\, developing ensembles of models for a real system\, and identifying the most promising candidates for practical applications. Examples will focus on heterogeneous catalysis—such as modeling catalysts and identifying active sites—but the methodologies are broadly transferable across materials science and related fields. \nTarget Audience: UCLA graduate students\, postdoctoral researchers\, and faculty in chemistry\, chemical engineering\, materials science\, physics\, and related disciplines. \nLearning Outcomes: \na. Understand the “accuracy versus scale” challenge in computational modeling of complex materials. \nb. Learn how neural network potentials can be trained on quantum mechanical data to achieve both high accuracy and scalability. \nc. Explore how machine learning based data screening can be used to identify the most promising candidates within large ensembles of models. \nd. Recognize UCLA resources (such as the Hoffman2 cluster) that can support ML-driven simulations in practice. \nThis workshop is hosted by IDRE Fellow\, Dr. Dongxiao Chen. \nRegister Now!
URL:https://workshops.ucla.edu/workshop/machine-learning-for-atomic-scale-materials-modeling-and-simulation/
LOCATION:Zoom
CATEGORIES:Training workshop / Tutorial
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