Biography
I am fascinated by emerging phenomena — how simple rules give rise to complex behavior, whether in vitro or in silico — and I find the field of artificial intelligence the ideal playground to study them.
As a postdoctoral researcher at AIRLab, Politecnico di Milano, I work on tabular foundation models, federated learning, and survival analysis, building models that learn from sensitive, multimodal data — turning scattered information into reliable, trustworthy predictions. I also love teaching and mentoring, and I take part in educational initiatives to promote AI literacy at all levels.
Research Interests
Tabular Foundation Models
Building general-purpose deep learning models for in-context learning on tabular data.
Survival Analysis
Predicting time-to-event outcomes for clinical data and financial applications.
Federated Learning
Training models across distributed, siloed datasets while preserving privacy.
Machine Learning for Healthcare
Applying AI to multimodal healthcare data, with a focus on privacy, interpretability, and reliability.
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Publications
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Citations
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Teaching Hours
Selected Publications
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Mastroleo, M., Archetti, Alberto, Mastroleo, F., Matteucci, M. (2026). SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks. 2026 International Joint Conference on Neural Networks (IJCNN).
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Archetti, Alberto, Lomurno, E., Piccinotti, D., Matteucci, M. (2025). FPBoost: Fully Parametric Gradient Boosting for Survival Analysis. European Conference on Artificial Intelligence (ECAI).
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Archetti, Alberto, Stranieri, F., Matteucci, M. (2024). Bridging the gap: improve neural survival models with interpolation techniques. Progress in Artificial Intelligence.
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Archetti, Alberto, Ieva, F., Matteucci, M. (2023). Scaling survival analysis in healthcare with federated survival forests: A comparative study on heart failure and breast cancer genomics. Future Generation Computer Systems.
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Archetti, Alberto, Matteucci, M. (2023). Federated Survival Forests. 2023 International Joint Conference on Neural Networks (IJCNN).
Experience
Research Fellow
Research topic — Tabular foundation models for healthcare.
National Ph.D. in Artificial Intelligence for Industry 4.0
Research topic — Federated Learning for Survival Analysis.
Collaborations — Research visitor at the Human Technopole, Milan, Italy.
Research Internship
Activities — Data analysis for anomaly detection; automated knowledge graph extraction.
Education
National Ph.D. in Artificial Intelligence for Industry 4.0
Thesis — Federated Survival Analysis: Ensemble and Neural Methods for Distributed Time-to-Event Data
Master of Science in Computer Science and Engineering
Thesis — Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*
Bachelor's Degree in Computer Science and Engineering
Diploma di Liceo Scientifico
Projects
AI-SPRINT
Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum
Role — Federated Learning advisor
Teaching Activities
AI Bootcamp
Advanced Deep Learning
AI Bootcamp
AI Product Management Bootcamp
Artificial Neural Networks and Deep Learning
Artificial Neural Networks and Deep Learning
AI Bootcamp
Coding Bootcamp
Software Engineering
Coding Bootcamp
Software Engineering
Coding Bootcamp
Software Engineering
Supervision
Pinar Erbil
Deep Variational Contrastive Learning for Risk Stratification and Time-to-Event Estimation
Marina Mastroleo
SurvKAN: Fully Parametric Survival Modeling with Kolmogorov-Arnold Networks
Sofia Perini
Exploring multimodality in federated survival analysis
Emanuele Paesano
A multimodal framework for survival analysis integrating clinical, genomic, histopathological and textual data
Niccolò Maria Rizzi
A generative pipeline for high-quality synthetic survival datasets
Gabriele Giusti
Multi-Agent Reinforcement Learning for emergent molecular communication in diffusion-based environments
Andrea Menta
Image restoration via Latent Neural Cellular Automata
Simone Cimmino
A pipeline for company industrial sector classification from unstructured website content
Sara Sacco
Analisi spettrale per il rilevamento di anomalie: studio sulla sopravvivenza degli estensimetri a corda per Snam S.p.A.
Talks
SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks
European Conference on Artificial Intelligence (ECAI)
Deep Variational Contrastive Learning for Joint Risk Stratification and Time-to-Event Estimation
European Conference on Artificial Intelligence (ECAI)
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis
European Conference on Artificial Intelligence (ECAI)
Discriminative adversarial privacy: balancing accuracy and membership privacy in neural networks
British Machine Vision Conference (BMVC)
Federated Survival Forests
International Joint Conference on Neural Networks (IJCNN)
Deep Survival Analysis for Healthcare: An Empirical Study on Post-Processing Techniques
AIxIA Workshop on Artificial Intelligence For Healthcare
Federated Survival Analysis
AIxIA Workshop on Machine Learning and Data Mining
Federated and Privacy-Preserving Learning
PhD Lecture, Politecnico di Milano
Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*
Conference on Machine Learning, Optimization, and Data Science (LOD)