In recent years, Daniel's research interest has specialized in prediction modelling, a cornerstone of 21st-century precision medicine that intersects statistics, health informatics, and clinical research. His group “Precision Medicine and Statistical Learning” is devoted to propelling precision psychiatry through the development of models for risk prediction, therapy response identification, and trials' enrichment. Key achievements include creating a transdiagnostic risk calculator for early psychosis detection and a model predicting major depression recurrence. Both these tools are under implementation and validation phases and show promise for clinical decision-making.