Author

Daniela de Albuquerque

Duke UniversityORCID 0000-0003-3736-1339Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Assay:electronic health records· 1 Assay:imaging· 1 Approach:statistical model· 1

Frequent Co-Authors

John PearsoniD· 2 shared Ganchao Wei· 1 shared Jack GoffinetiD· 1 shared Miles Martinez· 1 shared Rachael N. WrightiD· 1 shared Shiyang Pan· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 9 mutual co-author links

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2 shared papersNeuroscience

John PearsonORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintJun 23, 2026

Dynamic Compression Flows for Neuroscience Data

Ganchao Wei, Daniela de Albuquerque, Miles Martinez…·John Pearson

This paper introduces a dual flow approach for modeling neural data, producing more interpretable dynamics and better reconstructions than existing methods.

DOI 10.64898/2026.02.12.705535

electronic health recordsdeep learning

𝕏 PostBluesky

NeurosciencebioRxivPreprintApr 4, 2021

Deep Generative Analysis for Task-Based Functional MRI Experiments

Daniela de Albuquerque, Jack Goffinet, Rachael N. Wright, John Pearson

This paper introduces a generative model for task-based fMRI analysis that produces group and single-subject effect maps with improved flexibility and interpretability compared to standard methods.

DOI 10.1101/2021.04.04.438365

imagingstatistical model

𝕏 PostBluesky