Esther Hu
Software Developer at TekSynap
Dayton, Ohio, United States
Summary
🤩
Rockstar🎓
Top SchoolEsther Hu is an experienced full-stack software developer with eight years building performant web applications and backend systems across React, Angular, Spring Boot, Node.js, PHP, Python, and AWS. She has delivered measurable impact at defense and aerospace clients—optimizing rendering and data pipelines to cut load times and costs—and led solo projects typically handled by multi-person teams. Esther contributes to notable open-source .NET ML projects (TensorFlow.NET, NumSharp, BotSharp), adding core tensor ops, eager execution and classifiers while emphasizing extensive unit testing and reliability. Comfortable across the stack, she pairs production API and database work with automated testing and CI/CD practices to accelerate delivery. Based in Dayton, Ohio with a CS degree from Ohio State, she brings a practical mix of ML bindings experience and backend engineering that helps bridge research-grade tooling into .NET production systems.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Computer Science, 3.52/4.0, Bachelor's degree, Computer Science, 3.52/4.0 at The Ohio State University
Github Skills (21)
unit-testing10
net10
machine-learning10
dotnet10
numpy10
asp-net10
deeplearning-ai10
deep-learning10
tensorflow10
dotnet-core10
nlp10
csharp10
chatbot10
tf-idf9
onehot-encoding9
Programming languages (3)
Github contributions (5)
SciSharp/TensorFlow.NET
Jan 2019 - Feb 2021
.NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
Role in this project:
ML Engineer
ML EngineerContributions:27 reviews, 33 commits, 54 PRs in 2 years 2 months
Contributions summary:Esther primarily contributes to the core functionality of the TensorFlow.NET bindings, adding essential methods for operations like `FinishOperation`, `GetNodeDef`, and implementing `tf.matmul`, `tf.sub`, demonstrating a strong understanding of the TensorFlow API. Further contributions extend the bindings by adding eager execution functionality and integrating core mathematical operations, specifically focusing on bridging the gap between the .NET environment and the TensorFlow backend. These changes are vital for enabling .NET developers to develop, train, and deploy machine learning models using C# and F#.
deployingdotnetdevelopingscisharpdeep-learning
SciSharp/NumSharp
Nov 2018 - Nov 2018
High Performance Computation for N-D Tensors in .NET, similar API to NumPy.
Role in this project:Back-end Developer & QA Engineer
Back-end Developer & QA EngineerContributions:5 commits, 1 push, 5 comments in 13 days
Contributions summary:Esther primarily contributed to the implementation and testing of core functionalities related to the `NumSharp` library, which aims to provide NumPy-like capabilities in .NET. They added new features like `power` and `array` methods, improving the library's API to align with NumPy. The user also wrote extensive unit tests to validate these newly implemented methods, demonstrating a focus on ensuring the correctness and reliability of the library's functionality. In addition, the user added IEnumerator iterator.
pythonndarraynumbatensortensorflow
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.