Harsh Pathak

Staff Research Scientist at Snowflake

Bellevue, Washington, United States
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Summary

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Harsh Pathak is a Principal ML Scientist with a PhD in AI and Machine Learning and a decade of experience building production-ready ML systems across Snowflake, GoDaddy, and Expedia Group. He specializes in end-to-end solutions spanning ranking, NLP, vision, and classical ML, and has hands-on experience deploying high-performance models using TensorFlow, PyTorch, Spark and Databricks. At Expedia he delivered production super-resolution and GAN-based image enhancement work (including an IEEE-published attentional SRGAN variant) and scaled training across multi-GPU clusters. Based in Bellevue, WA, he combines research rigor with practical engineering—helping teams move prototypes to production while mentoring engineers on operational ML. Beyond applied work, his background shows a consistent focus on performant, scalable ML pipelines and memory-efficient model adaptations for large images.
code10 years of coding experience
job4 years of employment as a software developer
bookDoctor of Philosophy - PhD, Data Science, Doctor of Philosophy - PhD, Data Science at Worcester Polytechnic Institute
bookBachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at Vishwakarma Institute Of information Technology
languagesEnglish, Hindi
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Stackoverflow

Stats
101reputation
96kreached
4answers
1question
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Github Skills (105)

continuation10
language-model10
python10
deep-learning10
lora10
adaptation10
optimization10
optimization-methods10
convex-optimization10
ml10
tf-slim10
neural-network10
tensorflow10
roberta10
pytorch10

Programming languages (7)

JavaC++JavaScriptHTMLJupyter NotebookRubyPython

Github contributions (5)

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a9raag/apex

Jun 2016 - Oct 2020

Contributions:2 PRs, 8 pushes, 1 branch in 4 years 4 months
Awesome list for Neural Network Optimization methods.
Contributions:78 commits, 85 pushes, 1 branch in 2 years 1 month
neural-networkoptimization-methodsoptimizationdeep-learningnon-convex-optimization
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