School of Medicine
Showing 111-120 of 157 Results
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Javier Perez-Garcia
Postdoctoral Scholar, Epidemiology
BioJavier Perez-Garcia is a postdoctoral scholar in the Department of Epidemiology and Population Health at Stanford University. His research has been focused on the integration of multi-omic data (e.g., genomics, epigenomics, transcriptomics, and microbiome) to identify potential biomarkers of treatment response for complex diseases like asthma. His research background includes experience both in molecular biology techniques (e.g., DNA extraction and sequencing libraries preparation) and bioinformatic analyses (e.g., processing of raw omic data, association studies at genomic scale, or multi-omic integration through machine learning and quantitative trait loci analyses). He holds a Ph.D. in Health Sciences and a B.Sc. in Pharmacy from the University of La Laguna (Spain).
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Rita Popat
Associate Professor (Teaching) of Epidemiology
Current Research and Scholarly InterestsMy research interest focuses on the epidemiology of ParkinsonÂ’s disease and amyotrophic lateral sclerosis, specifically evaluating the genetic and environmental contributions to these neurodegenerative disorders. I am also interested in studying the relation of cognition, estradiol exposure (endogenous and exogenous), and genetic factors.
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Alexis Reeves
Instructor, Epidemiology and Population Health
BioAlexis is a Propel postdoctoral scholar in the Department of Epidemiology and Population Health in the School of Medicine with Dr. Michelle Odden’s lab. Her research is broadly focused on the causes and consequences of racial disparities in accelerated aging. She is particularly interested in the interplay of structural and interpersonal racism, and the psychobiological mechanisms in which they produce early health declines in minoritized populations. Her work to date has focused on the health of Black women as they enter into life-stages, such as the midlife menopausal transition, where cardio-metabolic risk is high. Alexis also has a strong interest in causal inference, and applies causal inference theory and methods to these areas of research to mitigate and quantify bias.
