Míriam Barrabés — accepted AI-conference papers

2026

Efficient Chromosome Parallelization for Precision Medicine Genomic Workflows

Daniel Mas Montserrat, Ray Verma, Míriam Barrabés, Francisco M. de la Vega, Carlos D. Bustamante, Alexander G. Ioannidis

AAAI 2026technical

Large-scale genomic workflows used in precision medicine can process datasets spanning tens to hundreds of gigabytes per sample, leading to high memory spikes, intensive disk I/O, and task failures due to out-of-memory errors. Simple static resource allocation methods struggle to handle the variabil

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2025

Feature Shift Localization Network

Míriam Barrabés, Daniel Mas Montserrat, Kapal Dev, Alexander G. Ioannidis

ICML 2025poster

Feature shifts between data sources are present in many applications involving healthcare, biomedical, socioeconomic, financial, survey, and multi-sensor data, among others, where unharmonized heterogeneous data sources, noisy data measurements, or inconsistent processing and standardization pipelin…

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2023

Adversarial Learning for Feature Shift Detection and Correction

Míriam Barrabés, Daniel Mas Montserrat, Margarita Geleta, Xavier Giró-i-Nieto, Alexander G Ioannidis

NeurIPS 2023poster

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods attempting to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in mult…

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