An exciting milestone for AI in science: Our C2S-Scale 27B foundation model, built with @Yale and based on Gemma, generated a novel hypothesis about cancer cellular behavior, which scientists experimentally validated in living cells.  With more preclinical and clinical tests,

What if LLMs could “read” & “write” biology? 🤔 Introducing C2S‑Scale—a @Yale + @GoogleAI @GoogleDeepMind collab: we scaled LLMs (up to 27 B!) to analyze & generate single‑cell insights by turning transcriptomes into text 🧬➡️📝 🔗 Blog: https://t.co/6jGBDBhP4z 🔗 Preprint:

Had an amazing time visiting the @broadinstitute and sharing insights at @MIA_at_Broad on "Single-Cell Analysis in the Age of LLMs"! Huge thanks to @spinar_d and @Schmidt_Center for hosting me. Catch the full talk here:

If you're at @HarvardPqg 'AI for Genomics and Health' today, come visit our poster on #cell2sentence! https://t.co/PpAwmuG9Jh

Proud of my (2nd year!) grad student @SyedARizvi5688 who gave an amazing primer on #LLMs and biological foundation models at the @broadinstitute @MIA_at_Broad seminar! 👏🎉

Can LLMs model continuous data flows? 🤔🌊📊 In our new paper “CaLMFlow: Volterra Flow Matching Using Causal Language Models”, we perform conditional flow matching using LLMs! 🧵👇 https://t.co/5BsqYQHZOv

How does complexity shape intelligence? 🤔 In our new paper Intelligence at the Edge of Chaos, we explore the relationship between complex systems and the emergence of intelligence in AI models. Can complexity alone unlock smarter systems? 🌌🧠 #AI #complexity

🚀 Beyond excited to announce our release of the #Cell2Sentence (C2S) API and new foundation models! 🎉 Our C2S API makes it incredibly easy to convert #singlecell data into cell sentences, perform inference with LLM-based C2S models, fine-tune them, and convert cell sentences

🚀 We are at @icmlconf in Vienna presenting #cell2sentence (C2S), the first framework for LLM-based single-cell foundation models! 🧬✨ C2S can generate cells from language prompts, interpret cells, and even generate natural language insights directly from data! 🔍💬📊 Stay

💡 Want to leverage the power of foundation models in graphs? 🔥 Introducing Foundation-Informed Message-Passing (FIMP), a framework for applying any pre-trained transformer-based foundation model to Graph Neural Networks! https://t.co/JEJTNV90L9

Major Cell2Sentence update 🎉🔬! We’ve been thrilled to see the attention Cell2Sentence has received from the single-cell community. Now, we’re excited to release our first update of Cell2Sentence (C2S) - a framework to leverage LLMs to train foundational single-cell models,

Accepted at #ICLR2024! BrainLM, the first #fMRI foundation model 🧠

We are excited to present our new paper exploring connections between deep learning and numerical analysis, framing neural networks as operators with fixed points! https://t.co/up7wgIvyn7

Excited to share one of my first works as a PhD student! We propose a new method allowing for language-pretrained LLMs to learn from single-cell data

Single Cells as text? We developed Cell2Sentence, a method that allows training of Large Language Models on single-cell data! https://t.co/IGc9TFXcTM With @danielflevine @SyedARizvi5688 @sachalevy3 @rahuldhodapkar @YaleSEAS @YaleMed #AI #ML #NLP #genomics #CompBio #singlecell

Introducing BrainLM 🧠🤖the first foundation model for #fMRI analysis trained on 6,700 hours of brain activity data! Fine-tune for specialized tasks or leverage zero-shot inference capabilities! @WuTsaiYale @YaleCompsci @YaleCBB @YaleMed https://t.co/MUobqXULfb

We are excited to present the Continuous Spatiotemporal Transformer (CST) at #ICML2023 on Wed at 5pm! https://t.co/Su92TEmA4Z Come talk to us at the poster or on the beach! 🏝️🏄🏖️

Thank you @mariabrbic for hosting me at @EPFL_en ! Amazing people and science, and beautiful campus! @epflSV @ICepfl @EPFLEngineering