Namrata Choudhury
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolNamrata Choudhury is a Machine Learning Engineer with seven years of experience building and optimizing ML and computer vision solutions on Intel architecture, currently based in Seattle. At Intel she has progressed from intern to full-time engineer, enabling efficient ML development across hardware accelerators and closing software–hardware gaps for autonomous and vision workloads. She contributes to open-source ML tooling—most notably enhancing PlaidML with backend, MLOps, and OpenVINO plugin integrations that expanded layer support and removed dependencies for demos. Comfortable across C++, Python, and systems-level optimization, she blends production-focused engineering with research-rooted rigor from her MS in Computer Science. Now taking time to travel and prototype long-standing ideas, she combines practical deployment experience with curiosity for novel ML applications. Her background in both classical vision algorithms and deep learning deployment gives her a rare end-to-end perspective from algorithm to accelerator.
7 years of coding experience
7 years of employment as a software developer
Bachelor of Technology - BTech Computer Science and Engineering, Bachelor of Technology - BTech Computer Science and Engineering at Heritage Institute of TechnologyHigh School Physics, High School Physics at South Point High School, KolkataMaster's degree Computer Science, Master's degree Computer Science at University of Florida
English, Bengali, Hindi
Github Skills (7)
python10
openvino10
mlops9
c-language8
cprogramming-language8
apim6
api6
Programming languages (1)
Github contributions (4)
plaidml/plaidml
Feb 2019 - Dec 2020
PlaidML is a framework for making deep learning work everywhere.
Role in this project:
Backend Developer & MLOps Engineer
Backend Developer & MLOps EngineerContributions:5 reviews, 20 commits, 23 PRs in 1 year 10 months
Contributions summary:Namrata contributed to adding parameters to the configuration file and made changes to the code to support a demo for MLSys. These changes include modifications to demo files, adding new functionality, and removing dependencies. Furthermore, the user worked on integrating OpenVINO plugins, specifically adding support for various layers like Abs, Tanh, Sigmoid, Log, Exp, AvgPool, MaxPool, Div, Subtract, Sin, Cos, and logical operations.
deep-learningplaidml
plaidml/openvino
Jun 2020 - Dec 2020
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
Contributions:7 reviews, 6 PRs, 15 pushes in 5 months
deep-learningopenvino
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