Eric Nguyen
@exnx
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Co-founder @ Radical Numerics PhD @ stanford @HazyResearch
Stanford, CA
Joined April 2009
✨ Excited to share a few life updates! 🎤 My TED Talk is now live! I shared the origin story of Evo, titled: "How AI could generate new life forms" TED talk: https://t.co/dh7iWcPaBu ✍️ I wrote a blog post about what it’s *really* like to deliver a TED talk blog:
ted.com
If DNA is just a string of letters, could AI learn to read it … or even write it? Bioengineering researcher Eric Nguyen reveals how AI has upended the rules of biology, potentially creating a future...
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We’re looking for a comp bio intern to work on next-gen, multimodal bio foundation models at Radical Numerics Our team designed & trained Evo and Evo 2. Now we’re going far beyond DNA Looking for experience w/ multi-omic data, NGS, and pipelines, and on-site in Palo Alto If
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🎙️ First time doing this 🙂 — I filled in for François on a one-off podcast episode with @alex_damian_ and @jerrywliu! We had a really fun, wide-ranging conversation about AI, theory, and how research actually gets done. Watch here 👇 https://t.co/vo6zX5nlFN
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That’s super cool!
What if you could build a gameplay with precision, then restyle it to look like anything - anime, fantasy, cyberpunk? Introducing Reverie: a real-time, programmable diffusion model trained for games, capable of conditioning beyond pixels. AI tools are only meaningful when they
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Sat down with Ian Fischer from @poetiq_ai, who just cracked ARC-AGI2 with just a 6 person team beating out all frontier labs for 20%! Crazy story. Youtube link below.
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I'm defending my PhD this week! Public zoom link and location info here: https://t.co/GJKKjrTt74
partiful.com
I'm defending my PhD! Which means there's one last presentation to graduate, and it's open to friends, family and the public! Feel free to come in person or watch online. Zoom link: https://stanfor...
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If you’re at #NeurIPS2025 today making it to the Virtual Cell workshop, please don’t miss the two orals by my team and collaborators and our posters. 1. Rbio (oral) Ana-Maria Istrate will talk about an approach to scientific reasoning that uses biomodels as reward sources by
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MLP fact storing research is awesome
Happy 🦃 Thanksgiving weekend! 🍂 This year, we cooked up a new recipe for juicy fact-storing MLPs. Instead of picking apart trained models, we asked: Can we construct fact-storing MLPs from scratch? 🤔 Spoiler: we can & we figured out how to slot these hand-crafted MLPs into
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Gross Domestic Intelligence, that's really cool
The U.S.–China AI race won’t be decided by who builds the most datacenters, but by who deploys the most intelligence. We call this Gross Domestic Intelligence (GDI): intelligence per watt × usable power. If the U.S. activates its dense installed base of local AI accelerators in
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What if we could autocomplete DNA based on function? Today in @Nature, we share semantic design—a strategy for function-guided design with genomic language models that leverages genomic context to create de novo genes with desired functions.🧵 https://t.co/P5qVJB3qIY
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(1/6) GPU networking is the remaining AI efficiency bottleneck, and the underlying hardware is changing fast! We’re happy to release ParallelKittens, an update to ThunderKittens that lets you easily write fast computation-communication overlapped multi-GPU kernels, along with new
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Go Simran!! Making amd go brr
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Intelligence per watt (IPW) is a really clean and simple framing. Great way to push this metric forward Really cool work by @HazyResearch
Data centers dominate AI, but they're hitting physical limits. What if the future of AI isn't just bigger data centers, but local intelligence in our hands? The viability of local AI depends on intelligence efficiency. To measure this, we propose intelligence per watt (IPW):
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AI has been built on one vendor’s stack for too long. AMD’s GPUs now offer state-of-the-art peak compute and memory bandwidth — but the lack of mature software / the “CUDA moat” keeps that power locked away. Time to break it and ride into our multi-silicon future. 🌊 It's been a
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you should watch this episode, some of my favorite ML thinkers https://t.co/2EWRDlvYlp
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This! The number of hours I spent looking for the sequences when reading papers, doing the detective work of tracing IDs back to zenodo files and NCBI downloads 🤦🏻♀️
Sharing sequence data should be as easy as toggling from private --> public. Finding sequence data in papers should be as intuitive as clicking a link. Scientists are hosting their sequence data on SeqHub to make them more reusable and discoverable. Explore them
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Awesome to have Michael talk about Radical Numerics on here. I keep telling him he should do more of these!
Thank you for having me, @DeltaInstitutes! We touched on a few topics, including the story behind @RadicalNumerics
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