F. Vankee Lin, PhD, RN, Lead PI 

Professor, Stanford University

Dr. Lin is a leader in research on brain aging and dementia. Her career is dedicated to understanding the neurophysiological mechanisms of cognitive impairment and designing non-pharmacological interventions to target those mechanisms to delay or prevent cognitive decline, particularly in individuals at risk for mild cognitive impairment and Alzheimer’s disease. 

Website

Ehsan Adeli, PhD

Clinical Assistant Professor, Stanford University

Dr. Adeli is the director of the Mind and Motion Lab (MML). He is affiliated with the Computational Neuroscience (CNS) Lab, Stanford AI Lab (SAIL), Stanford Vision and Learning (SVL) lab, and the Partnership in AI-Assisted Care (PAC). He is also an investigator in the Stanford Trustworthy AI group and the SAIL-Toyota Center For AI Research. He works at the intersection of machine learning, computer vision, computational neuroscience, healthcare, and medical image analysis.

Lab website

Adam Turnbull, PhD

Social Science Research Scholar, Stanford University

Dr. Turnbull is a senior scientist in the CogT lab with expertise in cognitive and brain aging, and neuroimaging and experience sampling methods. He is interested in improving non-pharmacological interventions by measuring and understanding participant states while they engage in interventions. The goal of his research is to develop personalized, adaptive, scalable behavioral interventions that can reach those most at risk for dementia, including those from lower socioeconomic or racial/ethnic minority backgrounds.

Tim Baran, PhD

Assistant Professor, University of Rochester

Dr. Baran is an imaging scientist who employs functional imaging modalities to identify imaging biomarkers that may allow for earlier diagnosis and improved treatment of Alzheimer’s disease, as well as biomarkers to identify neural correlates of successful cognitive aging. 

Lab website

Zhengwu Zhang, PhD

Assistant Professor, University of North Carolina at Chapel Hill

Dr. Zhang is a biostatistician with expertise in developing novel statistical and machine learning methods to extract knowledge from large neuroimaging datasets. His primary research interests lie in developing effective statistical and machine learning methods for high-dimensional “objects” with low-dimensional underlying structures. Most of his recent research focuses on developing state-of-the-art algorithms that address the modeling and computational challenges that often limit the accessibility of neuroimaging data from longitudinal, multi-site databases. 

Lab website