Ramith Hettiarachchi

Ramith Hettiarachchi

@ramith.fyi

PhD Student @CMUPittCompBio.bsky.social / @SCSatCMU.bsky.social Interested in ML for science/Compuational drug discovery/AI-assisted scientific discovery 🤞 from 🇱🇰🫶 https://ramith.fyi

788 followers1K following382 posts

every once in a while opus 5 gets angry with me for not listening to it "Separately — I’d really like an answer on committing." ok chill

For some reason the word tpu “chip” makes me feel like its tiny compared to nvidia gpus 😆 Been playing with v6e chips lately and pretty impressed

astra >> opus 5 (for biology). as a chat goes, Opus 5 keeps on saying > "Your framing was exactly right, and sharper than mine. Let me correct what I said." > "This corrects something I told you earlier."

alpha protein novo took 32 TPU chips for 5 days 😌 (equiv. 8xH200s for 46 days) meanwhile I'm playing with 8xL40s 🙉 😅🥲💀 there should be an easy way to train these models 🤔

I was venting about this in my family group 😆🥲 half an hour later my father shares an answer on perplexity for “how to reduce graphics processing time” ❤️😆🥹 gotto stay curious..

Does AF2 structure module transport the zero initialized frames to the endpoints? Weight tied 8 blocks or so 🤔 Feels like a generative model to some degree

A seq conditioned frame transport model.. if it can be viewed that way: counting the recycling, it’s like (3+1)*8 =32 step generation Only issue is that, during recycling, the frames are set back to 0,0,0… so it does learn how to move the frames pretty fast

been building an architecture explainer/explorer tool where you can go in varying depth of abstraction to understand: - started with the genie3 (in progress), then go towards AF2 Underneath, it's a compiler for turning model architecture descriptions into interactive diagrams scope.new.ramith.fyi

Interesting how ai generated music is easy to identify.. has something to do with the lyrics that sound like an essay and the frequency modulations of the voice🤔

Glad to have worked with this amazing team in organizing the GenBio workshop! ❤️ (There’s lot more who helped behind the scenes and couldn’t join in this picture) GenBio has been a great venue to bring researchers and foster very interesting convos.. wish I had more time to talk to even more ppl 😆

I should've posted updates more here 😅 all live tweeting went to genbio twitter handle 🥲 Maybe I’ll write a quick post summarizing what i learned from the talks and panel!

Registration line 😅 even after two days of registration.. ml conferences maybe reaching a breaking point 😆 i guess it makes more sense to have more decentralized events

So ESMFold2 uses 3DRoPE to enforce a geometry like pairbias (without the mem complexity of the standard pairbias) in its atom transformer… Wonder if the next stage, token transformer could’ve survived a similar approach (rather than the ln proj of z)

Glad to have contributed to this study on the computational side.. 😃 Thanks to implosion carving, we can now build devices that operate in the visible wavelength regime for classification tasks. Machine learning driven physical device design is pretty exciting.. news.mit.edu/2026/powerfu...

news.mit.eduPowerful shrinking technique could enable devices that compute with lightMIT researchers made 3D devices with nanoscale features that can perform optical computing tasks using visible light. They developed a technique that creates vacancies throughout a hydrogel, then shri...

Ramith Hettiarachchi reposted

Equivariance is dead! 😢 Or is it? 😈 Genie 3 is out! Our latest protein design model achieves SoTA results for binder design and motif scaffolding, greatly improving on BindCraft and Proteina-Complexa. It does so using all-atom SE(3)-equivariance based on a branched polymer representation👇

Yeqing Lin@yeqinglin.bsky.social

Introducing Genie 3, a generative protein model that substantially advances the state-of-the-art for binder design, increasing in silico success rates by up to 20x on hard multimeric targets. It also debuts a form of inference-time scaling unobserved in other design models. 🧵1/8

claude is good at making posters through html.. feels like doing everything programmically is better 😅 used to love editing stuff on illustrator

La proteina’s autoencoder can be thought of as a inverse folder to some degree 🤔 Wonder if just giving Ca, along with a random partial latent acts as a inverse folder (true we get the all atom structure as well)

noticing that backbone design models trained on AFDB 588K, when sampled through ODE results in poor designability (~25%). But through a proper noise schedule in SDE it goes to 90% (as reported in lit.) this felt counterintuitive, wonder if the reason is, AFDB being just 42% designable to begin with