Sam Huang @hynsam

CS PhD @Penn | AI Researcher | Prev @Alibaba_Qwen, @harvardmed yiningsamhuang.com Boston, MA Joined May 2022

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Sam Huang @hynsam

3 months ago

@YuanqiD @MSRNE Congrats🎉🎉🎉

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Sam Huang @hynsam

2 years ago

How to leverage protein structure and surface for fitness prediction? Introducing S3F at #NeurIPS2024 - augmenting PLM with structure and surface encoder for protein fitness prediction!

🤔 How does structural information help in protein fitness prediction? 🚀 Excited to share our work S3F (Sequence-Structure-Surface Fitness) model at #NeurIPS2024, a multimodal model for zero-shot protein fitness prediction! 🧬 #AIforScience Paper: arxiv.org/abs/2412.01108 1/🧵

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Zaixi Zhang @ZaixiZhang

2 years ago

Glad to share our updated survey paper on Geometric deep learning for Structure-based Drug Design (SBDD) arxiv.org/abs/2306.11768 The area is moving so fast and we may need to update every week 😂 @MengdiWang10 @marinkazitnik

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Sam Huang @hynsam

2 years ago

Science (AI)

The Nobel Prize @NobelPrize

2 years ago

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”

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Sam Huang @hynsam

2 years ago

AI for Science ❌ AI is Science ✅

The Nobel Prize @NobelPrize

2 years ago

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

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Sam Huang @hynsam

2 years ago

Excited to share our new paper ChemFlow has been accepted at #NeurIPS2024 !

Yuanqi Du @YuanqiD

2 years ago

🧵1/6 Happy to introduce ChemFlow: Navigating Chemical Space with Latent Flows arXiv: arxiv.org/abs/2405.03987 Demos are available at: colab.research.google.com/drive/1QAy_QoE… Flows can uncover meaningful structures of latent spaces learned by generative models!