Our paper (my first 😌) “Benchmarking and Improving LLM Robustness for Personalized Generation” was accepted to #EMNLP2025 findings🎉 🧑‍💻Data and Code: github.com/MichiganNLP/Be… Paper: arxiv.org/abs/2509.19358 🧵

Arxiv: arxiv.org/abs/2509.21837

Crazy how the MIT License started as a open source project and popped off so hard they built a university about it 🤯

Sleep hits harder when you’re exhausted. Food tastes better when you’ve gone hungry. Water tastes sweeter when you’ve been grinding. Music hits deeper when you’ve been in silence. Deprivation sharpens pleasure. Don’t run from suffering. That’s what gives life its color.

Initially you think you're training hard & smart enough to max out your potential. Then you get to high enough level to realize that, while a few people there do have more inborn talent than you, many of them have no inborn edge over you -- but they're way ahead because they

A general phenomenon that can sneak up on you when you’re at, say, the 99th percentile of a skill: At first, you’re exceptional enough that you receive praise from virtually everyone, and you may never go head-to-head with someone who can beat you. That is, until you join some

On today’s MLC-Ng Sunday Specials call, an old-timer returned after a research role at Max Planck l (undergrad at UNILAG); another casually mentioned his new role at Google. Joining these calls weekly is humbling-seeing so many talented folks doing big things from small places.

Thanks to this wonderful community, I got selected for the Google DeepMind scholarship for a Master's in Mathematical Science (AI for Science) at the African Institute for Mathematical Sciences, South Africa. Cheers to the next chapter🎉

It's a beautiful thing looking back to when I joined MLC last year. All I had was a scrambled mind and an interest in research. One step at a time; not an expert yet, but now I have direction and can confidently design and execute research projects thanks to ML Collective.

I’m recruiting PhD students for 2026! If you are interested in robustness, training dynamics, interpretability for scientific understanding, or the science of LLM analysis you should apply. BU is building a huge LLM analysis/interp group and you’ll be joining at the ground floor.

🚀 Federated Learning (FL) promises collaboration without data sharing. While Cross-Device FL is a success and deployed widely in industry, we don’t see Cross-Silo FL (collaboration between organizations) taking off despite huge demand and interest. Why could this be the case? 🤔