Yash V

San Francisco, California, United States

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Summary

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Rockstar

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Top School

Yash V is a software engineer based in San Francisco with nine years of experience building full-stack products and contributing to research-grade optimization software. He has shipped customer-facing features at startups and at Microsoft (notably on EsportsHub/start.gg) and most recently worked as a Senior Software Engineer at Atomic. His open-source work on pymoo demonstrates deep expertise in multi-objective optimization algorithms (R-NSGAIII, hypervolume metrics) and a comfort implementing performance-critical components in C++. Equally at home in React/TypeScript front-ends and numerical back-ends, he blends practical product delivery with algorithmic thinking. Outside of work he’s a coffee lover who enjoys turning complex research ideas into production-ready code.

9 years of coding experience

7 years of employment as a software developer

Master of Science - MS Computer Science, Master of Science - MS Computer Science at The University of Texas at Austin

North Farmington High School

Bachelor of Engineering (B.E.) Computer Engineering, Bachelor of Engineering (B.E.) Computer Engineering at Michigan State University

English, Marathi, Hindi, French

Github Skills (8)

algorithm10

algorithms10

python10

implement10

optimization10

numpy9

cprogramming-language9

c-language9

Programming languages (2)

TypeScriptPython

Github contributions (5)

anyoptimization/pymoo

Jun 2018 - Feb 2019

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

Role in this project:

Back-end Developer & Data Scientist

Contributions:15 commits, 7 pushes, 1 issue in 8 months

Contributions summary:Yash's commits primarily focus on updating and implementing features within the `pymoo` optimization library, including RNSGAIII and related algorithms. The code changes involve modifications to existing files like `run_experiment.py`, `rmetric.py`, and `rnsga_reference_line_survival.py`, indicating algorithm enhancements and performance metric improvements. The commits also involve implementing a hypervolume metric in C++, suggesting a focus on improving the library's assessment capabilities. The user demonstrates a deep understanding of multi-objective optimization, evident through the implementation of RNSGAIII.

evolutionheuristicsgenetic-algorithmsnmfdifferential

yashvesikar/DSP

Sep 2019 - Nov 2019

Contributions:25 commits, 24 pushes, 1 branch in 1 month

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