DO NOT GIVE UP! Our advocacy is working. A key Senate committee has indicated that it will reject Trump’s proposed cuts to science agencies including NASA and the NSF. Keep speaking up and calling your electeds 🗣️🗣️🗣️

Congratulations to Peter Holderrieth @msalbergo.bsky.social and Tommi Jaakkola for winning the best paper award for their work entitled "LEAPS: A discrete neural sampler via locally equivariant networks" at this year's Frontiers in Probabilistic Inference workshop #ICLR2025!

Excited to be at @iclrconf for #ICLR2025! I’ll give a talk at the Frontiers on Probabilistic Inference workshop to discuss work with @evdende2, @peholderrieth, @brianlee_lck, @jeha_paul, and Francisco Vargas! Let me know about your work, I will come by :)

We are thrilled to share the appointment of @sueyeonchung.bsky.social as an #KempnerInstitute Investigator, bringing her expertise in using #AI to understand #brain structure and function to @harvard.edu. Read the announcement: bit.ly/3PL3SEn

I am hiring a postdoctoral scholar with a start date summer or fall 2025. Projects will be focused on thermodynamically consistent generative models, broadly defined. If you’re interested, please send a CV and one paragraph about why you think you’d be a good fit to [email protected]

We got a preview of this stuff just a few days ago at the CECAM workshop. This is really cool stuff from @franknoe.bsky.social and co. Congrats!

Hellinger and Wasserstein are the two main geodesic distances on probability distributions. While both minimize the same energy, they differ in their interpolation methods: Hellinger focuses on density, whereas Wasserstein emphasizes position displacements.

If you're at NeurIPS next week come see our spotlight poster led by Yinuo Ren and Haoxuan Chen! We use the parallel sampling technique to rigorously establish a big acceleration for diffusion model inference! neurips.cc/virtual/2024...

Come to Cambridge -- lots of exciting things going on! There is a tenure-track position at Kempner and Harvard CS. Please share around: academicpositions.harvard.edu/postings/14362

A common question nowadays: Which is better, diffusion or flow matching? 🤔 Our answer: They’re two sides of the same coin. We wrote a blog post to show how diffusion models and Gaussian flow matching are equivalent. That’s great: It means you can use them interchangeably.

Thanks, Kyle!

Hello BlueSky! If and when I'm posting online, I'll be sure to do it here too.