PhD student in CS @ UMass Amherst | Research intern @ Flatiron Institute | Bayes + LLMs + Optimization
Joined March 2023
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Joohwan Ko retweeted
just 10 days left to apply: Flatiron Research Fellowship in the Center for Computational Mathematics! 2-3 years postdoctoral support for independently-led research at the Simons Foundation Flatiron Institute in NYC- join us!
apply.interfolio.com/193799
simonsfoundation.org/flatiro…
ALT molecular or atomic landscape with abstract electron/photon/atom-like objects in the landscape
Joohwan Ko retweeted
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
Joohwan Ko retweeted
Join @FlatironInst @SimonsFdn as a Research Fellow in NYC 🚀 Lead cutting-edge work in computational science & ML with top collaborators. Apply by Nov 15: apply.interfolio.com/193799
More on ML@CCM 👉 users.flatironinstitute.org/…
Keen to work with folks in #AIforScience & #GenerativeAI
Joohwan Ko retweeted
Public preview of BioEmu2! @MSFTResearch AI for Science’s new foundation model for biomolecular structure, dynamics and function. Open weights, MIT license.
github.com/microsoft/bioemu2
Movie: BioEmu2-simulated binding of intrinsically disordered peptide PUMA to MCL-1 receptor. 🧵⬇️
Joohwan Ko retweeted
🚨 New Paper Alert, 🎤 Accepted for an oral presentation at the NeurIPS 2026 STODY workshop!
📣 Introducing: Normalizing Flow Maps (NFM): Combining few-step transport from Flow maps with tractable likelihoods from Normalizing Flows.
This is my first work with my first crop of new PhD students, @francescoshox and @tim_hitge, who genuinely knocked this project out of the park.
📄 Paper: arxiv.org/abs/2610.22711
📝 Blog: the-bose-lab.github.io/norma…
💻 Code: github.com/francescoshox/Nor…
🧵
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Joohwan Ko retweeted
How can the math community forge the best path forward in the AI age? We're committing $15M to workshops and initiatives — plus free AI computing resources for foundation-supported researchers — through new partnerships with @AnthropicAI and @OpenAI: bit.ly/4zpfIbW
#math #science #AI
Joohwan Ko retweeted
📣 Our workshop on Emerging Directions in Probabilistic Modeling is next week, Oct 5–7 in NYC!
Full schedule: edpmworkshop.github.io/#sche…
Can't join in person? Talks will be livestreamed on Zoom. Register here: simonsfoundation.zoom.us/web…
📣 We are excited to announce the workshop
Emerging Directions in Probabilistic Modeling: Methods and Scientific Applications
📅 Oct. 5–7, 2026
📍 Flatiron Institute & IBM Research, NYC
Applications are welcome by Aug. 22:
edpmworkshop.github.io
Ever wondered how to make quasi-Newton methods practical for deep learning? Check out our new paper, SoftServe🍦!
Loved working on this with @dianarycai, @gowerrobert, and @tanyaisanumber during my Flatiron Institute internship this summer. Huge thanks to everyone!
How do we make quasi-Newton optimization methods practical for modern deep learning?
New preprint with @jooko303 @tanyaisanumber @gowerrobert on SoftServe -- a quasi-Newton method that designed for non-convex objectives, while also scaling to very large neural networks.
ALT SoftServe: a scalable quasi-Newton method for deep learning
Joohwan Ko retweeted
Tired to go back to the original papers again and again? Our monograph: a systematic and fundamental recipe you can rely on!
📘 We’re excited to release 《The Principles of Diffusion Models》— with @DrYangSong, @gimdong58085414, @mittu1204, and @StefanoErmon.
It traces the core ideas that shaped diffusion modeling and explains how today’s models work, why they work, and where they’re heading.
🧵You’ll find the link and a few highlights in the thread.
We’d love to hear your thoughts and join some discussions!
⚡ Stay tuned for our markdown version, where you can drop your comments!
