Jonathan Whitaker
Portland, Oregon, United States
Summary
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RockstarJonathan Whitaker is an R&D-focused data scientist and independent researcher based in Portland, Oregon with 11 years of experience bridging research, consulting, and teaching. He excels at unpacking deep technical ideas and translating them into practical ML solutions, a pattern reflected in roles at fast.ai, Hugging Face (Lead Course Instructor), and Answer.AI. Jonathan has contributed hands-on notebooks and course materials to high-profile diffusion model projects (Hugging Face and fastai), including from-scratch Stable Diffusion implementations and fine-tuning/inpainting workflows. Equally at home in the classroom and the lab, his background ranges from running short deep-learning courses to applied GIS work in Zimbabwe, showing a rare mix of pedagogy, open-source impact, and real-world domain experience.
11 years of coding experience
Github Skills (15)
transformers10
pytorch10
machine-learning10
jupyter-notebook10
stable-diffusion10
image-generation10
python10
diffusers10
fine-tuning10
autoencoders9
autoencoder9
clip9
enet9
huggingface8
huggingface-hub8
Programming languages (8)
Github contributions (5)
huggingface/diffusion-models-class
Nov 2022 - Jan 2023
Materials for the Hugging Face Diffusion Models Course
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 97 commits, 44 PRs in 2 months
Contributions summary:Jonathan contributed to the Hugging Face Diffusion Models Course by creating and refining notebooks for different units within the course. Their work involved drafting and improving notebooks demonstrating core concepts, including from-scratch implementations of diffusion models, and fine-tuning processes. The commits also added essential documentation, including links to external notebooks and scripts to illustrate the application of Stable Diffusion for various tasks such as inpainting.
stylegangenerative-modeltext-to-imagehugging-facediffusion
fastai/diffusion-nbs
Oct 2022 - Jan 2023
Getting started with diffusion
Role in this project:ML Engineer
ML EngineerContributions:2 reviews, 17 commits, 11 PRs in 3 months
Contributions summary:Jonathan's commits primarily involve the development and modification of a Jupyter Notebook focused on exploring Stable Diffusion. They are setting up the environment by installing necessary libraries, loading pre-trained models such as the VAE, tokenizer, and UNet, and defining a diffusion loop. Additionally, they made edits and added to the code in a way that would lead to the generation of an image from text. Their work focuses on understanding and implementing the core components of the Stable Diffusion pipeline, which aligns with the project description.
deep-learningdiffusionfastai
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