Wenhe Li
New York, New York, United States
Summary
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RockstarWenhe Li is a software engineer based in Mountain View with nine years of experience building web and ML tooling, currently at Microsoft after internships there. He has a strong open-source track record as an ML engineer and full-stack contributor to high-profile projects like TensorFlow.js and ml5.js, where he added capabilities such as CVAE support, Conv3DTranspose, noise layers, and robust WebWorker/fromPixels handling. A Cornell Tech graduate and ML Google Developer Expert, Wenhe blends frontend expertise (p5.js, browser pixel APIs) with core ML/linear-algebra work and testing infrastructure improvements. He has a history of contributing to large-scale JavaScript ML ecosystems and fixing cross-browser CPU/back-end issues—skills that help bridge research models and production browser deployments.
9 years of coding experience
Github Skills (16)
neural-network10
webgl10
tensorflowjs10
typescript10
deeplearning-ai10
javascript10
machine-learning10
typescripts10
deep-learning10
typescript-types10
testing10
k9
nodejs9
webassembly8
p5js8
Programming languages (18)
Github contributions (5)
tensorflow/tfjs
Jul 2019 - Apr 2021
A WebGL accelerated JavaScript library for training and deploying ML models.
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 10 PRs, 30 comments in 1 year 9 months
Contributions summary:Wenhe primarily contributed to the TensorFlow.js library by implementing new features related to noise layers and the Conv3DTranspose layer. These contributions involved writing new code, adding tests to ensure functionality and correctness, and modifying existing code to incorporate web worker support and fix issues in the CPU backend. They also worked on improving the testing infrastructure by adding fromPixels tests and improving web worker compatibility.
javascriptmlmodelwebgldeep-learningtypescript
ml5js/ml5-library
Apr 2019 - Dec 2019
Friendly machine learning for the web! 🤖
Role in this project:ML Engineer
ML EngineerContributions:31 commits, 12 PRs, 52 comments in 8 months
Contributions summary:Wenhe primarily contributed to the implementation of a Conditional Variational Autoencoder (CVAE) module within the ml5.js library. Their work included adding the necessary code for running pre-trained CVAE models, setting up latent dimension controls and random generation, along with updating related files for p5.js image support. The commits demonstrate the addition of a new machine learning model type to the library, expanding its capabilities.
machine-learninglstmdeep-learningimagenetjavascript
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