Author

Cathleen M. Nguyen

University of WashingtonORCID 0009-0008-3834-6230Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Assay:fluorescence / live-cell microscopy· 1 Organism:human· 1 Assay:imaging· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Andrew M. KahniD· 2 shared Bahetihazi MaiduiD· 2 shared Javier BermejoiD· 2 shared Juan C. del ÁlamoiD· 2 shared Manuel Guerrero‐HurtadoiD· 2 shared Pablo Martínez‐LegazpiiD· 2 shared Adithan KandasamyiD· 1 shared Alejandro GonzaloiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

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9 top collaborators · 34 mutual co-author links

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2 shared papersBioengineering

Andrew M. KahnORCID

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2 papers

Recent·Most cited

BioengineeringbioRxivPreprintSep 28, 2026

Joint Vector Flow Mapping and Segmentation: Ill-Posedness,Differentiable Bayesian Inference, and Synthetic Vortex-FlowBenchmarks

Juan C. del Álamo, Cathleen M. Nguyen, Manuel Guerrero‐Hurtado…·Pablo Martínez‐Legazpi

This paper introduces Bayesian VFM (B-VFM) to address ill-posedness in vector flow mapping by jointly inferring velocity fields, segmentation masks, and uncertainties using Bayesian inference.

DOI 10.64898/2026.09.24.754220

Bayesian inference

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BioengineeringComputers in Biology and MedicinePeer-reviewedApr 15, 2024

Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks

Bahetihazi Maidu, Pablo Martínez‐Legazpi, Manuel Guerrero‐Hurtado…·Juan C. del Álamo

This paper introduces AI-VFM, a physics-informed neural network method for super-resolution left ventricular flow and pressure mapping from echocardiographic data.

18 citations

humanimaging

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