Luca Miglior

Luca Miglior

PhD student in Computer Science at the University of Pisa. Working on deep generative models and graph machine learning.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors

Published in Computers in Biology and Medicine, 2025

A deep learning framework that classifies cardiorespiratory conditions from smartphone inertial sensor signals.

Recommended citation: Simone, L., Miglior, L., Gervasi, V., Moroni, L., Vignali, E., Gasparotti, E., & Celi, S. (2025). "Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors." Computers in Biology and Medicine, 196, 110595.
Download Paper

A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics

Published in Electronics, 2024

An acquisition protocol and Android application for measuring chest and abdomen respiratory kinematics from smartphone IMU sensors, validated on 77 healthy volunteers.

Recommended citation: Vignali, E., Gasparotti, E., Miglior, L., Gervasi, V., Simone, L., Haxhiademi, D., Frediani, L., Borelli, G., Berti, S., & Celi, S. (2024). "A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics." Electronics, 13(6), 1132.
Download Paper

Conference Papers

CompFlow: Composing Velocity Fields for Multi-Condition Generation

Published in 2nd Workshop on Compositional Learning (CompLearn) at ICML 2026, Seoul, South Korea, 2026

Summing conditional velocity fields at inference time implements a Product-of-Experts composition, enabling training-free generation under many simultaneous conditions.

Recommended citation: Miglior, L., Gervasi, V., & Bacciu, D. (2026). "CompFlow: Composing Velocity Fields for Multi-Condition Generation." 2nd Workshop on Compositional Learning at ICML 2026, Seoul, South Korea.
Download Paper

Towards Efficient Molecular Property Optimization with Graph Energy Based Models

Published in ESANN 2025 — 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2025

Energy-based models over molecular graphs for efficient property optimization.

Recommended citation: Miglior, L., Simone, L., Podda, M., & Bacciu, D. (2025). "Towards Efficient Molecular Property Optimization with Graph Energy Based Models." ESANN 2025 Proceedings, 289–294.
Download Paper