Author

Arvind Kumar

Stanford UniversityORCID 0000-0002-1804-2343Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:deconvolution· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Alexander G. IoannidisiD· 2 shared Carlos D. BustamanteiD· 2 shared Daniel Mas MontserratiD· 2 shared Helgi HilmarssoniD· 1 shared Míriam BarrabésiD· 1 shared Richa RastogiiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

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2 shared papersGenomics

Alexander G. IoannidisORCID

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2 papers

Recent·Most cited

GenomicsNature CommunicationsPeer-reviewedJul 25, 2026

High Resolution Ancestry Deconvolution for Next Generation Genomic Data

Helgi Hilmarsson, Arvind Kumar, Míriam Barrabés…·Alexander G. Ioannidis

This paper introduces Gnomix, a high-resolution ancestry deconvolution method for next-generation genomic data, achieving higher accuracy and faster performance than existing methods.

4 citationstop 1%

humanwhole-genome sequencing

𝕏 PostBluesky

GenomicsbioRxivPreprintApr 21, 2020

XGMix: Local-Ancestry Inference With Stacked XGBoost

Arvind Kumar, Daniel Mas Montserrat, Carlos D. Bustamante, Alexander G. Ioannidis

XGMix is a gradient boosted tree method for local ancestry inference that is accurate, simple to use, and fast to train on consumer-level hardware.

9 citations

GWAS summary statisticsmachine learning (classical)

𝕏 PostBluesky