Jason Antic
Lead AI Engineer at Climavision
Greenfield, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolJason Antic is a Lead AI Engineer with 15 years of experience who combines pragmatic deep learning research with production-grade engineering and product instincts. He launched the viral DeOldify project in 2018, co-created the NoGAN training technique presented at Facebook F8, and co-founded a bootstrapped startup whose colorization and restoration models are deployed on MyHeritage. His background spans shipping optimized ML pipelines in production, full‑stack and test automation work from a prior insurance-tech career, and even early experience as a weather forecaster — a mix that suits constrained, resourceful engineering. Jason remains an active open-source contributor (e.g., memory and fp16 optimizations for fastai diffusion notebooks) and has delivered mobile-friendly models such as a CoreML colorizer compatible with older iPhones.
16 years of coding experience
18 years of employment as a software developer
Bachelor of Science - BS, Computer Science major, minor in Mathematics, Bachelor of Science - BS, Computer Science major, minor in Mathematics at Penn State University
Stackoverflow
116reputation
53kreached
6answers
0questions
Github Skills (19)
pytorch10
python10
diffusion-models10
machine-learning10
stable-diffusion10
model-optimization10
cuda9
jupyter-notebook8
ai8
computer-vision7
syntax-highlighting6
database6
segment6
lua6
business-logic6
Programming languages (2)
Github contributions (5)
fastai/diffusion-nbs
Oct 2022 - Oct 2022
Getting started with diffusion
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 4 PRs, 10 comments in 8 days
Contributions summary:Jason primarily focused on optimizing memory efficiency within the diffusion model pipeline and modifying the example notebook. They removed unnecessary memory usage by replacing separate variable assignments and setting models to fp16. The user also updated example code to avoid safety filtering and fixed a typo to ensure the image generation loop worked correctly. Their contributions centered on improving the usability and performance of a diffusion model implementation.
deep-learningdiffusionfastai
jantic/KaggleCompetitions
Apr 2017 - Sep 2017
Contributions:45 commits, 40 pushes, 1 branch in 5 months
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