Guido Gagliardi

Post-Doctoral Researcher · STADIUS, ESAT, KU Leuven

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ESAT–STADIUS, KU Leuven

Kasteelpark Arenberg 10

3001 Leuven, Belgium

I build deep learning models for clinical neurophysiology that explain their own decisions — so clinicians can audit them, and so their explanations can teach us something new about disease.

I am a post-doctoral researcher at STADIUS, KU Leuven, in the group of Prof. Maarten De Vos, where I work on explainable deep learning for physiological signals. I hold a dual PhD with honors from KU Leuven and the Italian universities of Florence, Siena and Pisa, defended in May 2026. Before starting the postdoc I spent six months at Fraunhofer HHI in Berlin with Prof. Wojciech Samek’s AI department, working on attribution methods for time–frequency data.

My work is built around a single question: when a model reads a clinical signal well, what does it know that we do not? I develop methods that make that knowledge legible — prototypes, concept-based explanations, spectral attributions — and validate them against clinical guidelines and against disease. Most of it ships as open-source software: PhysioEx and ProtoSleepNet are the libraries my papers are built on, and they are what anyone else needs to reproduce or extend the results.


Research directions

Trustworthiness. Measuring performance beyond task accuracy: explaining the decision process, quantifying uncertainty so a model can abstain when the evidence is weak, and validating against clinical guidelines such as the AASM scoring manual.
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Knowledge discovery. Using AI not as an automation tool but as a scientific instrument — finding correlations in clinical data that were not known beforehand, and turning their explanation into domain knowledge about disease mechanisms.
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Foundation models and agentic AI. Engineering foundation models for neurophysiological signals with semantic embeddings, and agentic systems that ground language-model reasoning in verifiable clinical knowledge through retrieval.
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Currently

Extending prototype-based sleep analysis to narcoleptic mouse models with Prof. B. R. Kornum (University of Copenhagen), and to Alzheimer’s disease and REM Sleep Behaviour Disorder with the Center for Ear-EEG (Aarhus University). Writing Spectral Gradients, on disentangled time–frequency attributions, with Prof. W. Samek.

I am looking for tenure-track and junior-professor positions in Europe starting 2027.

news

Jun 01, 2026 Started as a post-doctoral researcher at ESAT–STADIUS, KU Leuven, in Prof. Maarten De Vos’ group.
May 27, 2026 Defended my PhD with honors — a dual degree from KU Leuven and the joint Smart Computing programme of the Universities of Florence, Siena and Pisa. :tada:
May 20, 2026 NeuroAtlas, our benchmark of foundation models for clinical EEG and brain–computer interfaces, is on arXiv and under review at NeurIPS 2026.
Apr 15, 2026 Guest lecture on Explainable & Trustworthy Artificial Intelligence at Ghent University (UGain / VAIA professional course).

selected publications

  1. Preprint v2
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    Prototype-based interpretable sleep staging with physiologically meaningful sub-stage pattern discovery
    Guido Gagliardi, Javier Garcia Ciudad, Letizia Micca, Birgitte Rahbek Kornum, Moran Gilat, Antonio Luca Alfeo, Mario G. C. A. Cimino, and Maarten De Vos
    2026
    Under review at npj Digital Medicine. The PDF is the revised manuscript (v2); version 1, posted on Research Square on 1 April 2026 under an earlier title, is available through the DOI
  2. Mach. Learn.
    Model-driven validation of visual explanations for multimodal emotion recognition
    Guido Gagliardi, Antonio Luca Alfeo, Vincenzo Catrambone, Mario G. C. A. Cimino, Maarten De Vos, and Gaetano Valenza
    Machine Learning, 2025
    (*) Selected as one of the three most important publications.
  3. Physiol. Meas.
    physioex.jpg
    PhysioEx: a new Python library for explainable sleep staging through deep learning
    Guido Gagliardi, Antonio Luca Alfeo, Mario G. C. A. Cimino, Gaetano Valenza, and Maarten De Vos
    Physiological Measurement, 2025
  4. Granul. Comput.
    Concept-wise granular computing for explainable artificial intelligence
    Antonio Luca Alfeo, Mario G. C. A. Cimino, and Guido Gagliardi
    Granular Computing, 2023
    (*) Selected as one of the three most important publications.