Jonathan Lorraine
Senior Research Scientist at NVIDIA
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolJonathan Lorraine is a research scientist with nine years of AI experience and a recent PhD from the University of Toronto, currently advancing generative AI at NVIDIA’s Toronto Spatial Intelligence lab. He has a strong track record in hyperparameter optimization, nested optimization, and learning in games, with 10+ papers at top ML conferences and over 1,000 citations. Jonathan has translated research into production impact at Google and Meta, notably designing a hyperparameter selection method that cut compute by ~10x while improving deployed model performance. He blends rigorous academic mentorship—years of TA experience and supervising other TAs—with cross-disciplinary collaboration in industry and a boutique hedge fund setting. Passionate about generative modeling and computer vision, he also channels creativity into digital art and hands-on hobbies like gardening and baking, reflecting a practical, experimental mindset.
9 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science; Machine Learning Group, Doctor of Philosophy (PhD) Computer Science; Machine Learning Group at Department of Computer Science, University of TorontoHonors Bachelor of Science (HBSc) Mathematics and Computer Science, Honors Bachelor of Science (HBSc) Mathematics and Computer Science at University of Toronto
German, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (21)
meta-learning9
hyperparameter-optimization6
language-modeling6
machine-learning6
recurrent6
3d5
multimodal5
language-model4
deep-learning3
neural-architecture-search3
pytorch3
architecture3
image-classification3
llm3
simulation2
Programming languages (3)
Github contributions (5)
lorraine2/ci-sandbox
Jun 2026 - Jun 2026
Contributions:41 PRs, 773 pushes, 397 branches in 1 day
lorraine2/hypernet-hypertraining
Feb 2018 - Jun 2018
Code for Stochastic Hyperparameter Optimization through Hypernetworks
Contributions:12 commits, 10 pushes, 1 branch in 3 months
hyperparameter-optimizationhypernetworksmeta-learning
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