I'm a machine-learning scientist and biomedical researcher with 5+ years building multimodal AI and signal-processing systems for neural, wearable, and clinical data. My pipelines span cohorts exceeding 2 million patients — from actigraphy-derived sleep biomarkers that flag Parkinson's disease risk a decade before diagnosis, to population-scale causal inference for stroke and dementia prevention.
GioSync is how that work reaches clinics: a product-engineering studio that turns messy sleep data into validated models, reproducible pipelines, and dashboards clinicians actually use. Currently a Data Analyst II at Mount Sinai; previously a research assistant in the Mignot Laboratory at Stanford.
Mission & vision
Turn messy clinical data into validated, deployable AI that clinics can actually trust and afford. Every model, pipeline, and dashboard is built for the realities of a working practice, not a research lab.
A future where every sleep clinic, regardless of size, has access to clinical-grade AI. Where clinicians spend their time with patients, not paperwork, and every model deployed is transparent, monitored, and accountable.
Education & affiliations
Università degli Studi di Genova
BSc Biomedical Engineering
Technical University of Denmark
MSc Biomedical Engineering
Stanford University
MS Epidemiology
Mount Sinai
Computational Data Analyst II