Luca Naef

Presented at #ACC26: Among patients with atherosclerotic cardiovascular disease, targeting an LDL cholesterol level below 55 mg per deciliter led to a lower 3-year risk of cardiovascular events than targeting a level below 70 mg per deciliter. Full Ez-PAVE trial results: nejm.org/doi/full/10.10… Editorial: Paving the Road toward Targeted Lipid Lowering nejm.org/doi/full/10.10… @ACCinTouch

@mkoeris If we want similar breakthroughs in other fields, the formula is simple: find ways to collect cheap low-resolution data alongside a few high-resolution datapoints, then train models to do the upscaling. (2/2)

We're growing rapidly at @RadicalNumerics and scaling our core teams. Join us in building the next generation of scientific world models. We're hiring across a few roles, each with significant ownership and cross-functional scope: - Member of Technical Staff, Post-Training - Member of Technical Staff, Infrastructure and Training Systems - Member of Technical Staff, Pretraining Science - Member of Technical Staff, AI Bio - Member of Technical Staff, Biosecurity Our technology brings together numerics, systems engineering, and architecture design to tackle large-scale pretraining on scientific data. Our blogs (see below) give a flavor of the work. We believe that advancing capabilities must go hand-in-hand with advancing safety and biosecurity. The same systems that design biology must also help defend against it. Ping me or others in the team if you'd like to learn more. job-boards.greenhouse.io/radicalnumerics

🚨 We’re Hiring in Generative AI for Biomolecular Design! 🧬🤖 We’re looking for Research Scientists working on biomolecular design, including small molecules, proteins, RNA, and molecular dynamics. Join us to help shape the next generation of AI-driven discovery, expanding our frameworks (e.g., Proteina-Complexa, La-Proteina, ReaSyn, and GenMol) to new domains and problems. If you’re excited about pushing the frontier of generative AI for biology and chemistry, we’d love to hear from you. 🔗 Apply: nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAEx… 🌐 Group page: research.nvidia.com/labs/genair/

Drug discovery has a scale problem. The complexes that govern signaling, regulation, and disease aren't single proteins, or even tidy pairs. They're sprawling assemblies of 10, 20, even 50+ subunits. The field is forced to sidestep that level of complexity, not for lack of interest, but for lack of tools. And yet, simulating cellular biology requires modeling the complexes and interactions these proteins form, not just individual proteins. NeoLink, @proximabio's proteomics platform, has experimentally mapped millions of protein-protein interactions across these higher-order complexes. Predicting their structures at this scale demanded something new. We built it with @NVIDIAHealth. By integrating NVIDIA's Fold-CP into our pipeline, we distribute computation for long biological sequences across multiple GPUs, removing the memory bottleneck that kept these complexes out of reach. New preprint out now with the NVIDIA BioNeMo team: bit.ly/4rDQnXA

Drug discovery has a scale problem. The complexes that govern signaling, regulation, and disease aren't single proteins, or even tidy pairs. They're sprawling assemblies of 10, 20, even 50+ subunits. The field is forced to sidestep that level of complexity, not for lack of interest, but for lack of tools. And yet, simulating cellular biology requires modeling the complexes and interactions these proteins form, not just individual proteins. NeoLink, @proximabio's proteomics platform, has experimentally mapped millions of protein-protein interactions across these higher-order complexes. Predicting their structures at this scale demanded something new. We built it with @NVIDIAHealth. By integrating NVIDIA's Fold-CP into our pipeline, we distribute computation for long biological sequences across multiple GPUs, removing the memory bottleneck that kept these complexes out of reach. New preprint out now with the NVIDIA BioNeMo team: bit.ly/4rDQnXA

Since it’s GTC week - good time to reintroduce what I do at @nvidia. I manage the investment portfolio of NVIDIA Ventures, working with startups across frontier model builders, techbio, robotics, AI infrastructure, materials discovery, energy, quantum and healthcare. I frequently draw on my training as a scientist and product manager for my job. My primary goal is to drive tech and ecosystem integration between NVIDIA and portfolio companies. Which can eventually lead to successful long term relationships. I am most interested in areas where AI translates into real world systems, especially in biology, robotics, and other scientific applications. More on here - nventures.ai