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publications
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.
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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.
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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.
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Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
Published in arXiv preprint, 2025
A benchmark for evaluating how well graph neural networks propagate information over long ranges.
Recommended citation: Miglior, L., Tolloso, M., Gravina, A., & Bacciu, D. (2025). "Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation." arXiv preprint arXiv:2512.17762.
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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.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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