Jonathan Lorraine Portfolio
Senior Research Scientist at NVIDIA Research (PhD, University of Toronto, advised by David Duvenaud), working on agentic AI for science and engineering with published work spanning provenance in video generation (MOTIVE), real-time world models (OmniDreams), and multimodal generation (LLaMA-Mesh).
Location
Didn't say. We're respecting the mystery.
Skills
What makes it work
A breakdown of the choices that make this portfolio stand out.
A stated diagnosis of the field, not just a topic list
Framing the site around "what limits modern AI now is direction, not capability" gives a specific, arguable position on where the field's bottleneck actually is, rather than a neutral summary of interests.
Named projects spanning distinct modalities under one research bet
MOTIVE (video provenance), OmniDreams (world models), and LLaMA-Mesh (3D generation) are different outputs unified by the same underlying interest in rewards and verification, showing a coherent bet applied across modalities.
Academic teaching listed alongside industry research
Teaching a full spread of ML courses (algorithms, neural networks, probabilistic reasoning, NLP) at a research university signals the research is grounded in fundamentals solid enough to teach, not just applied industry output.
What AI Researchers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
State a specific, arguable diagnosis of what actually limits your field right now — it is more memorable than a neutral list of research interests.
If your projects span different modalities or applications, name the single underlying bet that unifies them.
If you teach alongside your research, list the specific courses — it signals the work is grounded in fundamentals, not just the latest result.
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