Our lab is at the cutting edge of mental health research, dedicated to a holistic approach that integrates diverse data types—genetic, epigenetic, environmental, and psychosocial—using advanced AI and multimodal data fusion techniques.
Our Mission
We aim to transform mental health research by creating comprehensive models that reflect the complex interplay of factors influencing mental health. By integrating data from multiple domains, we strive to uncover novel biomarkers, elucidate underlying mechanisms, and develop personalized treatment strategies that can significantly improve mental health outcomes.
Key Research Areas
Multimodal Data Fusion: We combine data from various sources—genomic, environmental, psychosocial—using cutting-edge AI techniques to create integrative models. This approach allows us to understand the multifaceted nature of mental health conditions more comprehensively.
Genetic and Epigenetic Analysis: We analyze how genetic variations and epigenetic changes influence mental health. Our research draws on data from large-scale genomic databases to explore these relationships.
Environmental and Psychosocial Factors: We investigate how external factors, such as stress, trauma, and social environments, interact with genetic predispositions to impact mental health, aiming to identify crucial periods for intervention.
Personalized Treatment Development: By identifying novel biomarkers and understanding the mechanisms behind mental health disorders, we work towards developing personalized treatment strategies tailored to individual needs.