AlexΒ Shvets
Machine Learning Engineer at Palo Alto Networks
Cupertino, California, United States
Summary
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Top SchoolAlex Shvets is a Machine Learning Engineer with 8+ years of experience building end-to-end ML and deep learning solutions across computer vision, NLP, and healthcare domains, now based in Cupertino and currently at Palo Alto Networks. He has a strong research pedigree (PhD, >25 peer-reviewed papers, >2500 citations) and industry impact from roles at Microsoft and Zephyr AI where he shipped production models, led multimodal and survival-analysis projects, and ran large-scale offline experiments on 80M-patient EHR data. A Kaggle Master (top 100 worldwide) who also improves accessibility of open-source researchβe.g., enhancing documentation for a MICCAI-winning robotic-surgery segmentation repoβhe blends rigorous experimentation with product-focused deployment and monitoring. Comfortable across Python, Rust, PyTorch, Spark and cloud platforms, he pairs hands-on implementation with mentoring and reproducibility engineering to move models from prototype to production.
8 years of coding experience
11 years of employment as a software developer
Ph.D. Computational Physics, Ph.D. Computational Physics at University of StrasbourgMasterβs Degree Applied Mathematics and Physics, Masterβs Degree Applied Mathematics and Physics at National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
English, Russian, French, German
Github Skills (9)
content-management-system10
rs10
restructuredtext10
read-me10
content-management10
sphinxsearch10
sphinx10
documentation10
self-organization10
Programming languages (3)
Github contributions (5)
ternaus/robot-surgery-segmentation
Feb 2018 - Apr 2018
Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge
Role in this project:
Technical Writer & Content Strategist
Technical Writer & Content StrategistContributions:106 commits, 4 PRs, 33 pushes in 1 month
Contributions summary:Alex primarily focused on updating the project's documentation, specifically the README file. Their contributions involved refining the overview, adding installation instructions, detailing dataset organization, and explaining the training and evaluation processes. Furthermore, the user incorporated a demo example and citation information, significantly enhancing the project's usability and discoverability. These modifications reflect a focus on improving the project's documentation and accessibility for potential users.
improvementsegmentationcomputer-visionmiccaideep-learning
shvetsiya/geekbrains
Nov 2020 - Aug 2021
Contributions:9 reviews, 30 PRs, 31 pushes in 9 months
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