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EVENTS

AI and Flow Cytometry Workshop

A NIST–FDA–NIAID Co-Organized Workshop

This workshop aims to advance AI/ML applications in flow cytometry and related data. The workshop will focus on overcoming challenges and identifying solutions including essential measurements, reference controls, AI-ready reference data and ML/AI models. These efforts will target emerging applications in disease diagnosis, therapeutic development, manufacturing, and clinical trials as well as dosing and monitoring.

Day and Time	Session
Monday, June 9, 2025
10:30 am – 10:45 am EDT 7:30 am – 7:45 am PDT	Welcome • Lili Wang, NIST, Flow cytometry standardization enabling the production of high-quality cytometric datasets • Dawei Lin, NIAID/NIH, AI and immunology as a new research paradigm • Judith Arcidiacono, FDA, CBER standards recognition program and stakeholder engagement on standards
10:45 am – 11:45 am EDT 7:45 am – 8:45 am PDT	Setting the stage • Max Qian, J. Craig Venter Institute, Featured Presentation, The evolution of computational cytometry: milestones, challenges and opportunities • Thomas Liechti, ISAC, The potential of high-dimensional flow cytometry in human immunology research
12:00 pm – 1:00 pm EDT 9:00 am – 10:00 am PDT	Break
1:00 pm – 3:00 pm EDT 10:00 am – 12:00 pm PDT	Flow cytometry in research and the clinic • John Quinn, BD Life Sciences, Data-Driven Insights and Future Trends in Cytometry: AI, Automation, and Beyond • Guang Fan, Oregon Health & Science University, Development and Clinical Validation of Artificial Intelligence-Assisted Flow Cytometry for Acute Leukemia Diagnosis • Yu-Fen (Andrea) Wang, AHEAD Medicine, From noise to insights: translating speech AI advances for automated flow cytometry analysis • Kamila Czechowska-Kusio, Metafora Biosystems, Transforming Flow Cytometry with AI: Achieving Standardization and Reproducibility • Jansen Seheult, Mayo Clinic, Validating, Deploying, and Monitoring Flow Cytometry Pipelines for Minimal Residual Disease (MRD) Quantification in Blood Cancers • Panel Discussion
3:00 pm – 3:30 pm EDT 12:00 pm – 12:30 pm PDT	Break
3:30 pm – 5:00 pm EDT 12:30 pm – 2:00 pm PDT	Challenges and potential solutions for AI/ML applications • Holden Maecker, Stanford University, Featured Presentation, Methods for producing high-quality flow cytometry data • Ryan Brinkman, Dotmatics, SOULCAP: Enabling Trustworthy AI in Flow Cytometry Through Standardization and Objective Algorithm Evaluation • Santosh Putta, Revvity, Learning cell types across diverse flow cytometry data sets • Xing Qiu, University of Rochester, FastMix: Integrating Flow-cytometry, Gene Expression, and Clinical Data with Robust Mixed-Effects Models
Tuesday, June 10, 2025
10:30 am – 10:45 am EDT 7:30 am – 7:45 am PDT	Welcome, Day 2
10:45 am – 12:00 pm EDT 7:45 am – 9:00 am PDT	Flow data repositories and AI resources • Varun Chandola, National Science Foundation, Accelerating AI Innovation and Discovery through the National AI Research Resource (NAIRR) Pilot • Steven Kleinstein, Yale University, ImmPort: Enabling AI-Driven Analyses of Large-Scale Cytometry Data • Jonathan Irish, University of Colorado, ISAC Cytometry Data Repository (FlowRepository): Seeking, Identifying, & Targeting Clinically Significant Cells with Cytometry and Machine Learning
12:00 pm – 1:00 pm EDT 9:00 am – 10:00 am PDT	Break
1:00 pm – 3:00 pm EDT 10:00 am – 12:00 pm PDT	Updates on NIST FCSC interlaboratory studies and centralized data analysis • Lili Wang, NIST, Overview of TBMNK Cell Assay Interlaboratory Study • John Elliott, NIST, Update on the FCSC Interlaboratory Study Data Repository and Dissemination System (NIST-LabCAS) • Santosh Putta et al, Revvity, Centralized Data Analyses from Interlaboratory Studies • Panel Discussion
3:00 pm – 3:30 pm EDT 12:00 pm – 12:30 pm PDT	Break
3:30 pm – 4:30 pm EDT 12:30 pm – 1:30 pm PDT	Advanced technologies showcase presentations • Melvin Lye, Curiox, Automating Antibody Master Mix Preparation with C-FREE™ Pluto: Standardization for the Efficiency-Seeking Flow Cytometrist • John Nolan, Cellarcus Biosciences, Quantitative Analysis of Gene Delivery Vehicles Using Single Vesicle Flow Cytometry • Sean Hart, LumaCyte, Quantitative Cellular Analysis with Laser Force Cytology: Machine Learning for Predictive Bioprocessing • Willem Westra, ThinkCyte, Morphology Matters: Go Beyond Markers with VisionSort™ • Vidya Venkatachalam, Cytek Biosciences, Accessible Image Analysis: Learn, Adapt, and Conquer with Amnis® AI • Jason Lowery, Beckman Coulter Life Sciences, Advancing Flow Cytometry: An Overview of the Latest CytoFLEX mosaic Innovation • Sumona Sarkar, NIST, Standards for Cell Counting and Therapy Characterization • Paul DeRose, NIST, Reference Values for Fluorophore Concentration and Absolute Fluorescence Intensity • Edward Kwee/Jamie Almeida, NIST, Reference Data from Gene Delivery Systems Interlaboratory Studies to Support AI Models Predicting Function
4:30 pm – 4:45 pm EDT 1:30 pm – 1:45 pm PDT	Conclusion

Bioscience, Bioeconomy, Bioinformatics, Biometrology, Cell measurements, Protein engineering, Health, Biopharmaceuticals, Cell and gene therapy, Clinical diagnostics, Cancer, Precision medicine, Regenerative medicine and advanced therapy, Information technology, Artificial intelligence, AI measurement and evaluation, Applied AI and Machine learning

Created November 6, 2024, Updated June 24, 2025

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AI and Flow Cytometry Workshop

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A NIST–FDA–NIAID Co-Organized Workshop

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