The Computational Neuroscience Laboratory (CNSLAB) aims to identify phenotypes improving personalized medicine and prevention of neuropsychiatric disorders. The biomedical phenotypes are discovered by unbiased, machine learning-based searches across biological, neuroimaging, and neuropsychological data.

The multidisciplinary research team focuses on MRI studies of substance abuse, sleep, HIV, and adolescent brain development. The impact of the COVID-19 pandemic on adolescent brain development is a recent interest of ours. MRIs are acquired on scanners at SRI and in collaboration with other centers, such as those being part of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). Data curation and analysis is based on the software platform Scalable Informatics for Biomedical Imaging Studies (SIBIS).

The Stanford Translational AI (STAI) Lab unites interdisciplinary researchers from fields such as Artificial Intelligence (AI), Computer Vision (CV), Neuroscience, Medical and Neuro Imaging, and Psychiatry, with the goal of addressing critical challenges in Precision Healthcare and Mental Health. Our mission is twofold: to drive innovation both in AI methodologies and in their real-world clinical applications. Our group consistently publishes cutting-edge research in top-tier AI and CV conferences, while also translating these technologies into impactful clinical applications that are featured in leading medical and interdisciplinary journals. We focus on identifying technological gaps in clinical settings and strive to develop AI-driven solutions to bridge those gaps, with an emphasis on improving diagnosis, treatment, and patient outcomes. Please visit our Projects page to explore the full scope of our work and its potential to transform clinical practice through AI innovation.

We are a computational neuropsychiatry lab dedicated to developing computational methods to better understand the brain’s overall dynamical organization in healthy and patient populations. We employ algorithms from various fields, including Applied Mathematics, Econometrics, Machine Learning, Biophysics, and Network Science.