In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.

Our team employs advanced machine learning and deep learning techniques to address critical challenges in neurology and psychiatry. By analyzing a wide spectrum of data including neuroimaging, clinical, omics, psychometric, and behavioral inputs, we focus on enhancing diagnostic and treatment protocols across various conditions. Our efforts include integrating diverse data modalities such as EEG and MRI and exploring the potential of deep learning to improve predictive capabilities and data fusion techniques. Additionally, we address translational challenges, such as model explainability and data noise, aiming to overcome barriers that hinder the practical application of machine learning in clinical settings. Through these integrated approaches, we strive to advance personalized medicine in brain health.