Dominique Luna
SRE at Google
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolDominique Luna is an SRE at Google with 13 years of experience building high-performance, data-intensive systems and developer-facing tooling. Previously a full‑stack engineer and founder, Dominique launched Fast Data Analysis to process millions of rows in-browser using a Python/WebAssembly bridge and Arrow-Parquet serialization, cutting server costs to zero while preserving real-time interactivity. Their background includes simulation and ML content development for Udacity—implementing MPC in C++, building drone integration libraries, and teaching deep learning feature extraction with AlexNet—demonstrating a blend of applied research and production engineering. At Tread and earlier startups they optimized OCR, real-time queries, and scalable media ingestion, improving reliability and operational metrics across products. Known on GitHub for contributions to Udacity’s self-driving car projects, Dominique combines hands-on algorithmic work with pragmatic system design and an unusual knack for bringing large ML workflows into resource-constrained environments.
13 years of coding experience
7 years of employment as a software developer
Nanodegree, Machine Learning, Nanodegree, Machine Learning at UdacityBachelor's Degree, Computer Science, Bachelor's Degree, Computer Science at York University- Lassonde School of Engineering
English, Spanish
Stackoverflow
33reputation
1kreached
0answers
1question
Github Skills (23)
c-language10
python10
feature-extraction10
imagenet10
machine-learning10
model-predictive-control10
mpc10
mask-rcnn10
keras10
eigen10
deep-learning10
tensorflow10
computer-vision10
faster-rcnn10
cprogramming-language10
Programming languages (12)
Github contributions (5)
udacity/CarND-MPC-Project
Apr 2017 - Aug 2017
CarND Term 2 Model Predictive Control (MPC) Project
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:3 releases, 50 commits, 5 PRs in 4 months
Contributions summary:Dominique made various contributions to the project, including improvements to the core MPC (Model Predictive Control) logic and the main application logic. They updated the code, added documentation, and fixed typos, demonstrating a focus on code quality and project maintainability. Furthermore, the user implemented several significant additions such as adding Eigen for linear algebra and creating helper functions, indicating the capacity to introduce mathematical concepts into the project. They also worked on the project's setup and build process by including updated installation scripts.
mpcmodel-predictive-controlmpc-controlpredictiveterm
udacity/CarND-Traffic-Sign-Classifier-Project
Oct 2016 - Jul 2017
Classify Traffic Signs.
Role in this project:ML Engineer
ML EngineerContributions:34 commits, 25 PRs, 19 pushes in 8 months
Contributions summary:Dominique focused on implementing and experimenting with convolutional neural networks (CNNs) for traffic sign classification. They developed and refined Keras-based CNN models, including architectures leveraging techniques such as dropout, batch normalization, and ELU activation. The user also integrated image data augmentation using ImageDataGenerator to improve model performance. They also experimented with a feedforward network.
pythonsignstraffic-signstrafficclassify
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