Curriculum Vitae

Education

• Ph.D. in Computer Science (in progress), University of Pisa (2024–2027)
  Supervisor: Prof. Davide Bacciu

• M.Sc. in Computer Science, University of Pisa (2022–2024)
  Curriculum: Artificial Intelligence
  Final grade: 110/110 with honors
  Thesis: Implicit chemical property optimization with Graph Energy Based Models
  GPA: 30.4/30 (4.0/4.0)

• B.Sc. in Computer Science, University of Pisa (2018–2022)
  Final grade: 102/110
  Thesis: MyBreathingHeart: Sviluppo e Implementazione di un’applicazione per il monitoraggio remoto di problemi cardiorespiratori

• High School Diploma, Liceo Scientifico L.B. Alberti (2013–2018)

Academic Experience

• Teaching Assistant, Programming Laboratory 1 — University of Pisa (Jan–June 2025)

• AI Research Assistant, Department of Computer Science, University of Pisa (2023–2024)

• Undergraduate Fellowship (2023): Development and implementation of a smartphone-based deep learning framework for cardiopulmonary disease monitoring. Fully funded by Regione Toscana.

Research Interests

• Deep generative models — flow matching, diffusion, and energy-based models
• Compositional and controllable generation — training-free composition, guidance, multi-condition sampling
• Graph machine learning — message passing, long-range information propagation, benchmarking
• Generative models for molecules — property optimisation and molecular design
• AI for health — biosignals and wearable/smartphone sensor data

Publications

Journal Articles

1. E. Vignali, E. Gasparotti, L. Miglior, et al., “A new smartphone-based method for remote health monitoring: Assessment of respiratory kinematics,” Electronics, vol. 13, no. 6, 2024. DOI: 10.3390/electronics13061132
2. L. Simone, L. Miglior, V. Gervasi, et al., “Early screening of cardiorespiratory diseases through smartphone imu sensors and bidirectional lstm,” SSRN Preprint, 2023. DOI: 10.2139/ssrn.4676194

Conference Proceedings

• L. Miglior, L. Simone, M. Podda, D. Bacciu, “Towards Efficient Molecular Property Optimization with Graph Energy Based Models,” in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), January 2025. DOI: 10.14428/esann/2025.ES2025-120

• E. Vignali, L. Miglior, E. Gasparotti, et al., “An application for remote respiratory patterns detection in post-surgery patients,” in Proceedings of the 28th Congress of the European Society of Biomechanics, 2023. URL

Conferences and Workshops

• 2024: Workshop MyBreathingHeart: Quanto è in forma il tuo respiro? — Pisa, 9–10 February 2024