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PhD in Physics, Sapienza University of Rome, Italy
Thesis: “Physics-Inspired Inductive Biases for Classical and Quantum Machine Learning”
Group: Fisica AI&QC group
Supervisors: Stefano Giagu, Fabio Sciarrino
Andrea Cacioppo
Curriculum vitae - August 2026
Download PDFI am a physicist working on machine learning, with 6+ years of research and development experience across academia and industry. Currently, I am a researcher at Istituto Superiore di Sanità, Italy's national public health institute, where I build genomic-surveillance software and contribute to EU zoonoses reporting for EFSA. In parallel I keep an active research line in physics-inspired and quantum machine learning.
Education
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M.Sc. in Physics, Sapienza University of Rome, Italy
Thesis: “Deep learning for the parameter estimation of tight-binding Hamiltonians”
Supervisors: Stefano Giagu, Stefan Bauer
Grade: 109/110
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B.Sc. in Physics, Sapienza University of Rome, Italy
Thesis: “Hidden Markov model”
Supervisor: Luciano Pietronero
Grade: 110/110 with honors
Work experience
- present
Researcher, Istituto Superiore di Sanità, Italy
Dept. of Food Safety, Nutrition and Veterinary Public Health
Topics: machine learning for genomic surveillance of foodborne pathogens, development of platforms and bioinformatics tools, and maintenance of interactive zoonoses dashboards for EFSA
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Machine Learning Consulting, Italy & Switzerland
ML consulting applied to diverse industry problems.
- Primis Group SRL, Milan, Italy: determine best Machine Learning solutions tailored to LiDAR and satellite data, design of an anomaly detection algorithm for LiDAR data (contract of Rete Ferroviaria Italiana SPA)
- Hypercube SA, Lugano, Switzerland: application of Machine Learning techniques to the detection of time series anomalies
- Grid +, Rome, Italy: Automatic analysis of legal documents and anomaly detection
- Individual clients, Italy: training NNs to solve PDEs in finance - implementation of diffusion models - training NNs on incomplete datasets - invoice reconciliation using an online LLM
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Research Associate, Technical University of Munich, Germany
Topics: classical-quantum compound channels and algorithms for the automatic generation of quantum graph states
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Research Internship, Max Planck Institute for Intelligent Systems, Tübingen, Germany
Topics: Deep learning for estimating tight-binding Hamiltonians, quantum machine learning models and their connection with kernel methods
Teaching
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"Introduction to Machine Learning for Health Sciences", Istituto Superiore di Sanità, Italy
Task: lectures on deep learning and uncertainty estimation
"School of Python for genomics", Istituto Superiore di Sanità, Italy
Task: lab tutoring, advanced module on applying machine learning to genomics data
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"Python for scientific programming", Istituto Superiore di Sanità, Italy
Task: lectures and lab tutoring
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"Foundations of Programming with Laboratory", Sapienza University of Rome, Italy
Task: lectures and lab tutoring
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Mathematics, physics and computer science, Individual clients, Italy
Task: individual tutoring for university students
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"Quantum networking", Technical University of Munich, Germany
Task: tutoring
Students supervised
- Lorenzo Colantonio — "Quantum Diffusion generative models on variational quantum circuits" (Roma, 2023).
- Federico Scarpati — "Physics informed Graph Neural Networks for Graph Coloring Problems" (Roma, 2024).
Awards and grants
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Research grant, Sapienza University of Rome, Italy
"Development of quantum machine learning algorithms" - 1000 €
Excellence program for honor students, Sapienza University of Rome, Italy
Conference talks
Quantum Computing @ INFN, Milan, Italy, Talk
"Quantum enhanced fraud detection: a comparative study on real-world banking data"
Quantum Computing @ INFN, Padova, Italy, Talk
"Quantum diffusion models for quantum data learning"
QAIXIAQ2023 Workshop, Rome, Italy, Talk
"Quantum diffusion models using parameterized quantum circuits for data denoising"
ISIT, 2021 IEEE International Symposium on Information Theory, Talk
"Compound channel capacities under energy constraints and application"
Languages
- Italian - native
- English - fluent
- German - beginner
Skills
- Python, PyTorch, Linux, LaTeX, Git
Publications
- Andrea Cacioppo, Lorenzo Colantonio, Simone Bordoni, and Stefano Giagu. Hybrid and hardware-oriented approaches for quantum diffusion models. In 2025 International Joint Conference on Neural Networks (IJCNN), 2025.
- Lorenzo Colantonio, Andrea Cacioppo, Federico Scarpati, and Stefano Giagu. Efficient graph coloring with neural networks: A physics-inspired approach for large graphs. arXiv preprint arXiv:2408.01503, 2024.
- Andrea Cacioppo, Lorenzo Colantonio, Simone Bordoni, and Stefano Giagu. Quantum Diffusion Models. arXiv preprint arXiv:2311.15444, 2023.
- Andrea Cacioppo, Janis Nötzel, and Matteo Rosati. Compound Channel Capacities under Energy Constraints and Application. In 2021 IEEE International Symposium on Information Theory (ISIT), pages 640–645. IEEE, 2021.
- Andrea Cacioppo. Physics-Inspired Inductive Biases for Classical and Quantum Machine Learning. PhD thesis, Sapienza Università di Roma, Italy, 2026.
- Andrea Cacioppo. Deep learning for the parameter estimation of tight-binding Hamiltonians. Master’s thesis, Sapienza Università di Roma, Italy, 2020.