We are a computational neuropsychiatry lab, dedicated to developing methods for a better understanding of individual differences in brain functioning in healthy and psychiatric populations. We primarily work with noninvasive human neuroimaging data (fMRI, EEG, and fNIRS). We are currently working in these three areas – [a] Modeling spatiotemporal dynamics in brain activity to develop person- and disorder-centric biomarkers; [b] Understanding the role of brain dynamics for optimized learning and performance (e.g., creativity) in individual and team settings; and [c] Developing methods that use network science, machine learning, and signal processing for better understanding of brain dynamics.
Research focuses on developing analytic methods for biomedical data integration, with a particular interest in the spatial integration of radiology and pathology images. Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization. The radiology-pathology fusion allows the creation of rich spatial labels that can be used as input for advanced machine learning to predict the presence and aggressiveness of cancers.
Sherri Rose, Ph.D. is an Associate Professor of Health Policy and Co-Director of the Health Policy Data Science Lab at Stanford University. Her research is centered on developing and integrating innovative statistical machine learning approaches to improve human health and health equity. Within health policy, Dr. Rose works on risk adjustment, ethical algorithms in health care, comparative effectiveness research, and health program evaluation. She has published interdisciplinary projects across varied outlets, including Biometrics, Journal of the American Statistical Association, Journal of Health Economics, Health Affairs, and New England Journal of Medicine. In 2011, Dr. Rose coauthored the first book on machine learning for causal inference, with a sequel text released in 2018. She has been Co-Editor-in-Chief of the journal Biostatistics since 2019. Read more about the Health Policy Data Lab.
Our research in the Translational Cancer Evolution Laboratory focuses on the stochastic biological processes underlying cancer evolution with the goal to improve the prognosis and treatment of tumors. We develop computational methods to learn from large-scale biological data sets and design mathematical models to predict patient outcomes, generate novel hypotheses, and explain observations on a mechanistic level. We apply these methods to genomic data from clinically-annotated patient cohorts to advance precision medicine. For example, we have designed methods to optimize cancer early detection strategies and to find optimal combination therapies to minimize the risk of cancer relapse.