Research Activities
Application of advanced statistical methods, machine learning, deep learning, explainable AI (XAI), and data mining for predicting pathological mechanisms, identifying disease biomarkers and supporting patient stratification.
Management and harmonization of biomedical data through organization, curation, and integration of heterogeneous health and research datasets into standardized formats.
Implementation of studies that combine classical epidemiological methods with high-throughput technologies to reveal the intricate relationship between molecular profiles, environmental exposures, and disease risk.
Studying the dietary patterns and lifestyle behaviors of large cohorts of people of various ages and from different regions, and to analyze how these factors influence their health.
Development of metabolomic profiles that capture food‑specific metabolites and broader dietary patterns to identify objective biomarkers that accurately reflect nutrient intake and metabolic interactions.
Enhance dietary research by improving digital data‑collection tools, refining analytical methods, and developing robust techniques to handle challenges like missing questionnaire responses.



















