S
Stoyan Georgiev
Researcher at Google
Publications - 9
Citations - 688
Stoyan Georgiev is an academic researcher from Google. The author has contributed to research in topics: Rank (linear algebra) & Approximation algorithm. The author has an hindex of 5, co-authored 9 publications receiving 538 citations. Previous affiliations of Stoyan Georgiev include Stanford University.
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Journal ArticleDOI
Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia
Kevin Galinsky,Kevin Galinsky,Gaurav Bhatia,Po-Ru Loh,Po-Ru Loh,Stoyan Georgiev,Sayan Mukherjee,Nick Patterson,Alkes L. Price,Alkes L. Price +9 more
TL;DR: The FastPCA software is developed, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude.
Journal ArticleDOI
Abundant contribution of short tandem repeats to gene expression variation in humans
Melissa Gymrek,Thomas Willems,Audrey Guilmatre,Haoyang Zeng,Barak Markus,Stoyan Georgiev,Mark J. Daly,Alkes L. Price,Alkes L. Price,Jonathan K. Pritchard,Jonathan K. Pritchard,Andrew J. Sharp,Yaniv Erlich +12 more
TL;DR: A genome-wide survey of the contribution of short tandem repeats (STRs), which constitute one of the most polymorphic and abundant repeat classes, to gene expression in humans found that eSTRs are enriched in various clinically relevant conditions and may modulate certain histone modifications.
Journal Article
Adaptive Randomized Dimension Reduction on Massive Data
TL;DR: An approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages and adapts recent randomized low-rank approximation algorithms to provide an efficient solution to principal component analysis (PCA).
Posted ContentDOI
Fast principal components analysis reveals independent evolution of ADH1B gene in Europe and East Asia
Kevin Galinsky,Gaurav Bhatia,Po-Ru Loh,Stoyan Georgiev,Sayan Mukherjee,Nick Patterson,Alkes L. Price +6 more
TL;DR: FastPCA as mentioned in this paper leverages recent advances in random matrix theory to accurately approximate top PCA while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude.
Posted ContentDOI
Fast principal components analysis reveals convergent evolution of ADH1B gene in Europe and East Asia
Kevin Galinsky,Gaurav Bhatia,Po-Ru Loh,Stoyan Georgiev,Sayan Mukherjee,Nick Patterson,Alkes L. Price +6 more
TL;DR: Using the PC-based test for natural selection, this work replicates previously known selected loci and identifies three new genome-wide significant signals of selection, including selection in Europeans at the ADH1B gene.