معرفی
Seyed Mostafa Kia is an Assistant Professor in the Department of Cognitive Science and Artificial Intelligence at Tilburg University, Netherlands. He holds a PhD in Computer Science from the University of Trento and has extensive postdoctoral experience at Radboud University Medical Center and University Medical Center Utrecht. His work focuses on scalable, explainable machine learning solutions for precision medicine, particularly in mental health and neuroimaging. Dr. Kia leads projects like 'MEGaNorm' and 'AI Deployment Journey in Healthcare,' aiming to translate AI insights into clinical practice. His research integrates normative modeling, federated learning, and uncertainty estimation to address heterogeneity in psychiatric and neurological disorders. He teaches courses including 'Methods for Responsible AI' and collaborates internationally on neuroimaging studies.
Education: PhD (Computer Science, University of Trento, 2017), Master’s (CIMeC, University of Trento, 2013), Bachelor’s (Ferdowsi University of Mashhad, 2007).
Research Interests: Machine learning for precision medicine, interpretable AI, neuroimaging analysis, normative modeling frameworks, and ethical AI deployment in healthcare. Key methodologies include hierarchical Bayesian regression, transfer learning, and deep learning applied to psychiatric disorders, Alzheimer’s disease, and brain dynamics.
Recent work emphasizes longitudinal studies of cortical thickness, white matter heterogeneity, and predictive models for psychosis treatment outcomes. His projects address scanner variability, healthy aging, and patient-centric AI tools for mental wellbeing.
Grants & Projects: Principal investigator in projects such as 'Normative Range of P600 Morphology' and 'Charting the Normative Electroencephalography in Healthy Aging Population,' alongside collaborative initiatives like the ENIGMA consortium.
Labs/Teams: Active in the Cognitive Science and AI Academic Collaborative Center for Digital Health & Mental Wellbeing at Tilburg University, contributing to interdisciplinary collaborations across neuroscience and AI ethics.
