Alexander G Ioannidis — 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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2024

HyperFast: Instant Classification for Tabular Data

David Bonet, Daniel Mas Montserrat, Xavier Giró-i-Nieto, Alexander G. Ioannidis

AAAI 2024technical

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives…

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2023

Adversarial Attacks on Genotype Sequences

Daniel Mas Montserrat, Alexander G. Ioannidis

ICASSP 2023accepted

Adversarial attacks can drastically change the output of a method by small alterations to its input. While this can be a useful framework to analyze worst-case robustness, it can also be used by malicious agents to damage machine learning-based applications. The proliferation of platforms that allow…

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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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2020

Lai-Net: Local-Ancestry Inference with Neural Networks

Daniel Mas Montserrat, Carlos D. Bustamante, Alexander G. Ioannidis

ICASSP 2020accepted

Local-ancestry inference (LAI), also referred to as ancestry deconvolution, provides high-resolution ancestry estimation along the human genome. In both research and industry, LAI is emerging as a critical step in DNA sequence analysis with applications extending from polygenic risk scores (used to…

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