F. Vankee Lin, PhD, RN

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. 

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.

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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. 

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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. 

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Yize Zhao, PhD

Assistant Professor, Yale University

Dr. Zhao is a biostatistician whose research focuses on the development of statistical and machine learning methods to analyze large-scale complex data (imaging, -omics, EHRs), including Bayesian methods, feature selection, predictive modeling, data integration, missing data and network analysis. Her research interests lie in biomedical research areas, particularly cancer, mental health, and cardiovascular disease.

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Benjamin Risk, PhD

Assistant Professor, Emory University

Dr. Risk is a biostatistician whose research focuses on neuroimaging and aims to further scientific understanding and medical research by developing, improving, and disseminating statistical methodology. His research 

Biostatistics; neuroimaging; cognitive science; MRI; spatio-temporal processes; dimension reduction; multivariate statistics; independent component analysis; environmental statistics; functional data analysis

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Deqiang Qing, PhD

Associate Professor, Emory University

Dr. Qiu is an imaging scientist with interest in the development and application of advanced imaging techniques and computational modelling to advance our understanding of human brain functioning, as well as to improve human healthcare. Dr. Qiu currently leads the Computational Neuroimaging & Neuroscience Lab (CN2L). His research focuses on the development and translation of novel neuroimaging methods (including MRI and PET) to the clinic and answering neuroscience questions using imaging techniques. 

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Kristin Linn, PhD

Assistant Professor, University of Pennsylvania 

Dr. Linn's primary research interest is the development of statistical methods for analyzing large neuroimaging data sets with the goal of understanding disease processes in the brain. Dr. Linn is actively working on statistical frameworks for harmonizing imaging data from multi-site studies and estimating spatially varying patterns of multimodal image associations. Dr. Linn also has experience designing sequential multiple assignment randomized trials (SMARTs). She is particularly interested in using data from SMART designs to estimate personalized dynamic interventions that improve long-term outcomes for individuals. 

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