School of Medicine
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Alexander Ioannidis
Postdoctoral Research Fellow, Biomedical Data Sciences
BioAlexander Ioannidis (PhD, MPhil) graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil in Computational Biology and Diploma in Greek from the University of Cambridge. His Ph.D. from Stanford University was in Computational and Mathematical Engineering, where he teaches machine learning and data science. He also has an M.S. in Mgmt. Sci. and Eng. (Optimization) from Stanford. Prior to Stanford, he worked in superconducting computing logic and quantum computing at Northrop Grumman. As a current research fellow in the Stanford School of Medicine (Department of Biomedical Data Science), his work focuses on applying computational methods to problems in genomics and population genetics.
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I work on novel algorithm design (particularly ancestry related) for several large-scale genomic studies that aim at understanding genetic causes of disease.
I also focus on projects at the intersection of history and population genetics, including work with native communities. As the grandson of Cappadocian refugees expelled from their native land, I try to engage with the complex sentiments of displaced indigenous peoples in these projects. Pain over the disruption of community heritage and over dispossession from traditional sites often remains raw. If engagement with descendant communities is lacking, research into our past can often feel like a continuation, even a legitimation, of dispossession. Combined alongside a dialogue with native communities, however, genetics can play a small role in helping to reclaim ancestral stories and dispersed ancestral connections. I hope our work in this area plays a constructive role in that process.
As written by the poet Rumi in the language of the Cappadocians (Rûm),
پیمی تیِ پَاثیِسْ پیمی تی خاسِس
“Tell me what happened to you, tell me what you have lost.”
[Rumi; Konya ms 67; translit. πε με τι έπαθες, πε με τι έχασες] -
John P.A. Ioannidis
Professor of Medicine (Stanford Prevention Research), of Epidemiology and Population Health and by courtesy, of Statistics and of Biomedical Data Science
Current Research and Scholarly InterestsMeta-research
Evidence-based medicine
Clinical and molecular epidemiology
Human genome epidemiology
Research design
Reporting of research
Empirical evaluation of bias in research
Randomized trials
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science