Alessandra Pescina

I am a research assistant with a background in Mathematical Engineering. My work lies at the intersection of data science and population health, with a focus on chronic disease progression in aging cohorts.

I’m particularly interested in designing analytical methods that integrate modern machine learning with classical epidemiological models, while preserving interpretability and clinical relevance.

Education:

• 2022-2025 Master's Degree in Mathematical Engineering - Statistical Learning at Politecnico of MIlan, Italy. Thesis: "Multi-state models with intermittent observation scheme in the aging research field."
• 2019-2022 Bachelor's Degree in Mathematical Engineering at Politecnico of MIlan, Italy.

My research focuses on developing a semi-supervised deep learning framework to improve the modeling of disease onset in population-based aging studies. A key challenge in epidemiological cohorts is that many disease transitions are not directly observed but must be inferred from periodic clinical visits or incomplete follow-up.

To address this, I integrate generative AI with multi-state modeling to infer unobserved disease onset and reconstruct individual health trajectories, even when data are sparse or irregularly collected. Ultimately, the objective is to provide the aging research community with more robust and flexible tools for analyzing longitudinal health data and for gaining deeper insight into complex disease dynamics in older populations.