Ron Do
Professor With Tenure, Windreich Department Of Artificial Intelligence And Human Health at Icahn School of Medicine at Mount Sinai
New York, New York, United States
Summary
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Senior🎓
Top SchoolRon Do is a tenured professor and Charles Bronfman endowed chair specializing in personalized medicine, statistical and population genetics, leading large-scale genomics and data science at Mount Sinai. He directs the Center for Genomic Data Analytics and the Do Laboratory, which focuses on deciphering genetic and biological bases of complex diseases in ethnically diverse, EHR-linked biobanks to advance precision medicine. With a PhD from McGill and prior training at MGH/Harvard as a Banting Fellow, he has authored over 130 high-impact publications including lead papers in Nature Genetics, JAMA, Lancet, Nature and NEJM. His work bridges methodological innovation and clinical translation, developing tools to extract actionable insights from population-scale genomic data. A recipient of institutional research awards, he combines rigorous statistical genetics with an atypical emphasis on diversity-aware inference and electronic health record integration. Based in New York, he runs a lab that consistently pushes both computational methods and clinical application toward more equitable genomic medicine.
8 years of coding experience
8 years of employment as a software developer
MSc, Experimental Medicine, MSc, Experimental Medicine at The University of British ColumbiaPhD, Human Genetics, PhD, Human Genetics at McGill University
Github Skills (6)
randomization10
sum10
histogram8
python7
statistics6
shiny2
Programming languages (1)
Github contributions (5)
rondolab/MR-PRESSO
Jun 2017 - Jan 2021
Performs the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) method.
Contributions:1 review, 43 commits, 3 PRs in 3 years 7 months
pythonmethodresidualoutliermendelian-randomization
rondolab/Genetic-priority-score
Nov 2022 - Jan 2024
Create Shiny app
Contributions:2 PRs, 31 pushes in 1 year 1 month
shiny-appshiny
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