Ryan Gillard
Staff Research Scientist at Google
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top School
Top expert inGoogle Cloud Platform Development Essentials
Ryan Gillard is a Staff Research Scientist at Google with over 15 years of programming experience and eight years focused on applied AI/ML in healthcare and enterprise settings. He holds a PhD in Physics and blends deep quantitative modeling, optimization and large-scale simulation expertise to improve operational decision-making—having built adaptive scheduling and resource models for Naval hospitals and value-based care. At Google he advances research-to-production ML, and his open-source contributions include adding autoencoders and GANs to notable Google Cloud ML education repos, demonstrating practical generative modeling work used in ML immersion curricula. Known for turning complex physics and healthcare problems into performant, deployable algorithms, he pairs rigorous academic training with a knack for engineering high-performance data pipelines and solvers.
8 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at Wayne State UniversityBachelor of Science (B.S.), Neuroscience, Bachelor of Science (B.S.), Neuroscience at University of Michigan
English, French
Github Skills (18)
google-cloud-platform10
python10
dcgan10
variational-autoencoder10
machine-learning10
cgan10
autoencoder10
deep-learning10
tensorflow10
cyclegan10
data-preprocessing9
jupyter-notebook8
computer-vision8
bigquery7
trainings7
Programming languages (3)
Github contributions (5)
GoogleCloudPlatform/practical-ml-vision-book
Jan 2021 - Jul 2021
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 1 PR, 10 pushes in 6 months
Contributions summary:Ryan contributed significantly to the project by adding Autoencoders (AEs) and Generative Adversarial Networks (GANs). Their work involved creating, configuring, and implementing these machine learning models within the project. Specifically, the changes include the introduction of autoencoders and variational autoencoders (VAE) and their related code for their implementation, which includes model architectures and loss functions.
GoogleCloudPlatform/asl-ml-immersion
Feb 2018 - Mar 2018
This repos contains notebooks for the Advanced Solutions Lab: ML Immersion
Role in this project:ML Engineer
ML EngineerContributions:8 commits in 1 month
Contributions summary:Ryan primarily contributed to the implementation and modification of machine learning models within the repository. Their work involved adjusting model parameters, preprocessing data, and creating datasets for model training and evaluation, as evident in the changes to the notebook files. The user also made minor fixes and updated project details.
pythondata-sciencegoogle-cloud-platformimmersionmachine-learning
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.