Jonathan Lorraine

Jonathan Lorraine, a research scientist at NVIDIA's Learning and Perception Research group, focuses on AI for science, developing agents that propose, run, and verify their own experiments, and on measurement discipline for trustworthy results. His research includes generative models for 3D, video, and audio, amortized and bilevel optimization, hyperparameter optimization, and training-data attribution. He is an author of ATT3D, LATTE3D, LLaMA-Mesh, Graph Metanetworks, and Score Distillation Sampling for Audio, and completed his PhD at the University of Toronto under David Duvenaud.

Jonathan Lorraine is a research scientist at NVIDIA, in the Learning and Perception Research group. His current work is on AI for science: agents that propose, run and verify their own experiments, and the measurement discipline that makes their results trustworthy. His research spans generative models for 3D, video and audio, amortized and bilevel optimization, hyperparameter optimization, and training-data attribution. He is an author of ATT3D and LATTE3D on amortized text-to-3D synthesis, LLaMA-Mesh on 3D mesh generation with language models, Graph Metanetworks, and Score Distillation Sampling for Audio. He completed his PhD at the University of Toronto with David Duvenaud, on scalable nested optimization for deep learning.