Excited to introduce flow Q-learning (FQL)! Flow Q-learning is a *simple* and scalable data-driven RL method that trains an expressive policy with flow matching. Paper: arxiv.org/abs/2502.02538 Project page: seohong.me/projects/fql/ Thread ↓

We red-teamed modern LLMs with practicing clinicians using real clinical scenarios. The LLMs: ✅ Made up lab test scores ✅ Gave bad surgical advice ✅ Claimed two identical X-rays looked different Here’s what this means for LLMs in healthcare. 📄 arxiv.org/abs/2505.00467 🧵 (1/)

Paper reviewing can be easy if we follow these simple rules: 1. Summary: A good rule of thumb is to add enough content so that the summary can’t apply to any other paper and highlights the details and claims from the work, and its novelty. 1/3

New OpenProblems paper out! 📝 Led by Malte Lücken with Smita Krishnaswamy, we present openproblems.bio – a community-driven platform benchmarking single-cell analysis methods. Excited about transparent, evolving best practices for the field! 🔗 nature.com/articles/s4158…

Announcing Ambient Protein Diffusion, a state-of-the-art 17M-params generative model for protein structures. Diversity improves by 91% and designability by 26% over previous 200M SOTA model for long proteins. The trick? Treat low pLDDT AlphaFold predictions as low-quality data

🎉Personal update: I'm thrilled to announce that I'm joining Imperial College London Imperial College London as an Assistant Professor of Computing Imperial Computing starting January 2026. My future lab and I will continue to work on building better Generative Models 🤖, the hardest

1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025! 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling Learn more and submit → fpiworkshop.org

Online Now! Bacterial ADP-heptose triggers stem cell regeneration in the intestinal epithelium following injury dlvr.it/TLsYzs #stemcells

We're excited to introduce our new work on mature mRNA property prediction, co-first authored with the amazing Ian Shi and Taykhoom Dalal. We introduce mRNABench to standardize evaluation and present a study on building more efficient RNA foundation models. 🧵

Really excited to share that we’re in the Y Combinator Combinator summer batch! I’m even more excited to be teaming up with Phil Fradkin and Ian Shi on this next chapter. Blank Bio, we're building the next generation of foundation models for RNA.

Come check out SBG happening now! W-115 11-1:30 with charliebtan Joey Bose Chen Lin Leon Klein Michael Bronstein

we’re not kfc but come watch us cook with our feynman-kac correctors, 4:30 pm today (july 16) at ICML Conference poster session — east exhibition hall #3109 Kirill Neklyudov Alex Tong Tara Akhound Sadegh Viktor Ohanesian

📢Presenting SDE Matching🔥🔥🔥 🚀We extend diffusion models to construct a simulation-free framework for training Latent SDEs. It enables sampling from the exact posterior process marginals without any numerical simulations. 📜: arxiv.org/abs/2502.02472 🧵1/8

A bit late, but excited to announce our new paper, "Defining and benchmarking open problems in single-cell analysis," a joint effort led by our team (Scott Gigante, Daniel Burkhardt ) at Yale University and the Fabian Theis and Malte Luecken at Helmholtz! 🚀 **Open Problems**, is a living,

reprogramming cells with transcription factors is our most expressive tool for engineering cell state traditionally, we found TFs by ~guesswork ICML Conference we're sharing NewLimit's SOTA AI models that can design reprogramming payloads by building on molecular foundation models

Wrapping up #ICML2025 on a high note — thrilled (and pleasantly surprised!) to win the Best Paper Award at GenBio Workshop @ ICML25 🎉 Big shoutout to the team that made this happen! Paper: Forward-Only Regression Training of Normalizing Flows (arxiv.org/abs/2506.01158) Mila - Institut québécois d'IA

AI4Mat is back for NeurIPS! time to crystallize those ideas and make a solid-state submission by august 22, 2025 💪 new this year: opt-in your work for our Research Learning from Speaker Feedback program -- a new structured discussion format where spotlight presenters receive

Blank Bio (Blank Bio) is building foundation models to power a computational toolkit for RNA therapeutics, starting with mRNA design and expanding to target ID, biomarker discovery, and more. ycombinator.com/launches/O8Z-b… Congrats on the launch, Jonny, Phil Fradkin & Ian Shi!

The news is out! We're starting Blank Bio to build a computational toolkit assisted with RNA foundation models. If you want to see my flip between being eerily still and overly animated check out the video below! The core hypothesis is that RNA is the most customizable molecule