Shubham Chandak

Senior Applied Scientist, Annapurna ML at Amazon Web Services (AWS)

Hyderabad, Telangana, India
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Summary

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Senior
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Top School
Shubham Chandak is a Senior Applied Scientist at AWS's Annapurna ML team with nine years of experience bridging research and production in machine learning, data compression, and bioinformatics. He earned a PhD and MS in Electrical Engineering from Stanford (advised by Prof. Tsachy Weissman) after a B.Tech from IIT Bombay, and has applied his academic expertise to Amazon S3 and Annapurna ML projects since 2021. His work spans practical systems engineering and deep theory—particularly information theory and genomic data compression—and includes industry internships at Philips and MIT. He has taught data compression as an adjunct lecturer at Stanford, signaling a commitment to translating research into pedagogy and practice. Based in Hyderabad, he combines rigorous academic training with hands-on experience building ML-driven storage and sequencing solutions. A less obvious strength is his cross-domain fluency: from hardware crypto implementations to DNA storage, he navigates both low-level systems and abstract information-theoretic problems.
code9 years of coding experience
job3 years of employment as a software developer
bookIndian Institute of Technology Bombay
bookDoctor of Philosophy - PhD Electrical Engineering, Doctor of Philosophy - PhD Electrical Engineering at Stanford University
languagesHindi, English
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Github Skills (59)

multivariate-timeseries10
package-management10
fastq-files10
conda10
forge10
fastq10
codec10
sequencing10
bioinformatics10
compression10
fasta10
vaapi10
bioconda10
libfdk-aac10
conda-forge9

Programming languages (9)

C++ShellCSSCSCSSJavaScriptHTMLJupyter Notebook

Github contributions (5)

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Contributions:23 commits, 22 pushes, 2 branches in 1 year 6 months
shubhamchandak94/libbsc

Aug 2017 - Apr 2020

High performance block-sorting data compression library
Contributions:1 release, 3 pushes, 2 tags in 2 years 8 months
compressionsortingcompression-libraryperformancefst
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