{"id":5672,"date":"2025-09-02T13:14:49","date_gmt":"2025-09-02T17:14:49","guid":{"rendered":"https:\/\/ai.jhu.edu\/ssec\/?p=5672"},"modified":"2026-01-09T13:35:04","modified_gmt":"2026-01-09T18:35:04","slug":"2022-workshop-for-large-scale-scientific-imaging","status":"publish","type":"post","link":"https:\/\/ai.jhu.edu\/ssec\/2025\/09\/02\/2022-workshop-for-large-scale-scientific-imaging\/","title":{"rendered":"2022 Workshop for Large-Scale Scientific Imaging"},"content":{"rendered":"

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\u00a0June 16, 2022\u00a0– June 17, 2022<\/p>\r\n\r\n

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On June 16th and June 17th, the Scientific Software Engineering Center (SSEC) at JHU hosted its inaugural workshop on large-scale scientific imaging in conjunction with the launch of the SSEC, operating as part of the broad AI-X effort and funded by Schmidt Futures.<\/h3>\r\n\r\n

The area of large-scale scientific imaging forms one of the pillars of the emerging AI revolution in science, as the explosion of scientific data is mostly due to the advancement of imaging technologies.<\/p>\r\n\r\n

This topic is the common thread across almost all scientific disciplines, cancer research, medicine, including radiology, brain research, astronomy, climate observations, materials science, cell biology. Furthermore large scale numerical simulations can create ultra-high-resolution images of observable phenomena\u2014from turbulence to climate models and ocean circulation patterns.<\/p>\r\n\r\n

The difficulty arises in that most of these data sets are well past the Terabyte scale with even mid-scale instruments capable of generating several petabytes per year. Currently the handling of these data sets is balkanized, mostly done in a one-off fashion by the experiments.<\/p>\r\n\r\n

This workshop brought together different research communities to address these outstanding problems, discuss commonalities, identify differences, and discover how one can use better economies of scale in creating and managing such large image archives. The goal is that the collated, cross-disciplinary knowledge will lead to develop ways to interface these image archives to existing AI environments and shorten the path to create novel AI applications for training and inference.<\/p>\r\n\r\n

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Below is a list of the speakers who presented at the workshop, along with access to select speakers\u2019 slide decks.<\/h3>\r\n\r\n

Please note: these videos are unlisted on YouTube and are not available via any search methods. The only way to access the SSEC presentation content is via the links here.<\/p>\r\n\r\n

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Session 1<\/h2>\r\n\r\n

K.T. Ramesh,\u00a0Whiting School of Engineering<\/em>
Introduction<\/strong><\/p>\r\n\r\n

Alex Szalay,\u00a0Krieger School of Arts & Sciences, IDIES<\/em>
Introduction<\/strong>\u00a0(PDF<\/a><\/strong>,\u00a0PPT<\/a><\/strong>)<\/p>\r\n\r\n

Denis Wirtz,\u00a0Whiting School of Engineering<\/em>
CODA: 3D reconstruction of tumors and whole organs and organisms at single-cell resolution using AI<\/strong><\/p>\r\n\r\n

Mitra Taheri,\u00a0Department of Material Science & Engineering<\/em>
Microscopy as a Platform to Accelerate Machine Learning<\/strong><\/p>\r\n\r\n

Charles Meneveau,\u00a0Department of Mechanical Engineering & IDIES<\/em>
Democratizing access to massive high-fidelity simulation data in fluid turbulence\u00a0<\/strong>(PDF<\/strong><\/a>)<\/p>\r\n\r\n

Ian Dobbie,\u00a0Krieger School of Arts & Sciences, Biology<\/em>
Fluorescence microscopy from simple imaging to big data<\/strong>\u00a0(PDF<\/a><\/strong>)<\/p>\r\n\r\n

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Left\/Top(mobile):\u00a0Denis Wirtz explaining the process for reconstructing tumors and organs down to the individual cell using AI;\u00a0<\/em>Right\/Bottom(mobile):\u00a0Ian Dobbie presenting on fluorescence microscopy<\/em><\/p>\r\n\r\n

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Session 2<\/h2>\r\n\r\n

Alex Baras,\u00a0School of Medicine<\/em>
Challenges, opportunities, and ultimately the promise of what a digital transformation holds for histopathology<\/strong>\u00a0(PPT<\/a><\/strong>)<\/p>\r\n\r\n

Andrew J Connolly,\u00a0Univ Washington<\/em>
Searching below the noise: hunting for asteroids in astronomical data\u00a0<\/strong>(PDF<\/a><\/strong>,PPT<\/a><\/strong>)<\/p>\r\n\r\n

Brian Caffo,\u00a0Bloomberg School of Public Health<\/em>
Density regression for functional MRI connectomics\u00a0<\/strong>(PDF<\/a><\/strong>)<\/p>\r\n\r\n

David Elbert,\u00a0Hopkins Extreme Materials Institute (HEMI)<\/em>
Image Scale and Speed in High-Throughput Materials Discovery and Autonomous Experimentation<\/strong><\/p>\r\n\r\n

Nick Andresen,\u00a0School of Medicine<\/em>
Generating a Temporal Bone Image Database for Studying Hearing and Balance Disorders\u00a0<\/strong>(PPT<\/strong><\/a>)<\/p>\r\n\r\n

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Session 3<\/h2>\r\n\r\n

Janis Taube,\u00a0School of Medicine<\/em>
AstroPath: Astronomy accelerates Pathology<\/strong>\u00a0(PDF<\/a><\/strong>)<\/p>\r\n\r\n

Ani Thakar,\u00a0Krieger School of Arts & Sciences, IDIES<\/em>
SDSS: The Imaging Survey That Started It All<\/strong>\u00a0(PDF<\/a><\/strong>)<\/p>\r\n\r\n

Mike Miller,\u00a0Biomedical Engineering<\/em>
Molecular Computational Anatomy: Biomedical Data Science at Scale<\/strong><\/p>\r\n\r\n

Brandon Lane,\u00a0National Institute of Standards and Technology<\/em>
Image-based In-situ Process Monitoring Data from Laser Powder Bed Fusion (LPBF) Additive Manufacturing<\/strong><\/p>\r\n\r\n

Elana Fertig,\u00a0School of Medicine<\/em>
Inferring spatial tumor and immune interactions with SpaceMarkers<\/strong>\u00a0(PDF<\/strong><\/a>)<\/p>\r\n\r\n

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Janis Taube speaking on the Astropath platform and its considerations going forward<\/em><\/p>\r\n\r\n

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Session 4<\/h2>\r\n\r\n

Wilfred Ngwa,\u00a0School of Medicine<\/em>
Image-guided drug delivery with Biomaterial drones during radiotherapy\u00a0<\/strong>(PDF<\/a><\/strong>)<\/p>\r\n\r\n

Thomas Haine,\u00a0Earth & Planetary Sciences<\/em>
Visualizing Ocean Circulation Model Solutions<\/strong>\u00a0(PDF<\/a>,\u00a0KEY<\/a><\/strong>)<\/p>\r\n\r\n

Duncan Sousa\u00a0School of Medicine, Beckman Center for CryoEM<\/em>
Cryo-EM: High Throughput Data Acquisition and Processing (PDF<\/a>,\u00a0PPT<\/a>)<\/strong><\/p>\r\n\r\n

Sarah Jordaan,\u00a0School of Advanced International Studies<\/em>
Solving problems at the interface of land and energy infrastructure*<\/strong><\/p>\r\n\r\n

Gerard Lemson,\u00a0Krieger School of Arts & Sciences, IDIES<\/em>
Indexing Big Data in the Database: Cosmological Simulations and Image Archives*<\/strong>\u00a0(PDF<\/strong><\/a>)<\/p>\r\n\r\n

*Not included in YouTube recordings<\/p>\r\n\r\n

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