F. Vankee Lin, PhD, RN, Lead PI 

Clinical 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. 

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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

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

Andrew Anderson, PhD

Research Assistant Professor, University of Rochester

Dr. Anderson researches how different memory types are processed in the brain during language comprehension. He uses functional neuroimaging and electrophysiological methods to measure brain activity and computational models to decode them. His current projects focus on how information processing changes in the aging brain, and how this can inform early detection of cognitive impairment. 

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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

Adam Turnbull, PhD

Research Associate, University of Rochester

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