Mihir Paradkar
Software Engineer at Google
New York City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolMihir Paradkar is a software engineer and statistical data scientist with 11 years of experience building full-stack systems and ML tooling, currently at Google in the New York City area. He blends rigorous mathematical training (Columbia and Cornell) with applied work across ads optimization at Yelp, medical-data imputation research, and production engineering at Google. Mihir contributes to prominent open-source ML projects like TensorFlow GNN, improving core documentation, model transformations, and developer ergonomics—work that reveals a focus on both correctness and usability. Comfortable across backend services, web front ends, and graph neural network models, he brings a cross-disciplinary perspective rooted in biology, physics, and operations research. Colleagues rely on him to translate complex statistical ideas into robust, debuggable software that scales in production.
11 years of coding experience
2 years of employment as a software developer
High School, High School at Lakeland High SchoolMathematics, Mathematics at Columbia University in the City of New YorkBachelor's Degree with Honors, Biological Engineering, Minors in Computer Science, Operations Research, and Electrical Engineering, 3.923/4.0, Bachelor's Degree with Honors, Biological Engineering, Minors in Computer Science, Operations Research, and Electrical Engineering, 3.923/4.0 at Cornell University
Github Skills (6)
machine-learning10
tensorflow10
graph-neural-network10
python10
gnn10
deep-learning9
Programming languages (8)
Github contributions (5)
tensorflow/gnn
Jan 2022 - Jan 2023
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
Role in this project:
ML Engineer
ML EngineerContributions:16 releases, 1 review, 76 commits in 1 year
Contributions summary:Mihir contributed to the development of Graph Neural Networks within the TensorFlow GNN library. Their work focused on improving the documentation, specifically addressing docstrings in the `GraphTensor` class to accurately reflect the library's behavior. Further contributions included the addition of a transformation from `GCNConv` to prepare for the integration of the GCNConv model, demonstrating involvement in core model development within the library. The user also worked on adding `__repr__` methods to various classes for improved debugging and usability.
gnndeep-learningneural-graphneural-networksmachine-learning
mihirparadkar/chainer
Nov 2017 - Mar 2018
A flexible framework of neural networks for deep learning
Contributions:36 pushes, 3 branches in 3 months
flexible-frameworkdeep-learningneural-networksmachine-learningneural-network
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.