Nature: US senators poised to reject Trumpβs proposed massive science cuts
Committee gives first hint that policymakers might preserve, rather than slash, funding for US National Science Foundation and other agencies.
DO NOT GIVE UP!
Our advocacy is working.
A key Senate committee has indicated that it will reject Trumpβs proposed cuts to science agencies including NASA and the NSF.
Keep speaking up and calling your electeds π£οΈπ£οΈπ£οΈ
11.07.2025 19:03 β π 1356 π 453 π¬ 8 π 24
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!
28.04.2025 04:53 β π 8 π 1 π¬ 0 π 0
See you there!
24.04.2025 01:34 β π 2 π 0 π¬ 0 π 0
Excited to be at @iclrconf for #ICLR2025! Iβll 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 :)
24.04.2025 01:34 β π 7 π 1 π¬ 0 π 0
Iβll go!
24.04.2025 01:34 β π 0 π 0 π¬ 0 π 0
very much agree!
22.02.2025 14:23 β π 1 π 0 π¬ 0 π 0
itβs gotten so hard. if one can find a reading group that chooses topics well I think thatβs how I learn best at least. Then some of the selection is outsourced.
07.01.2025 21:53 β π 5 π 0 π¬ 1 π 0
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 youβre interested, please send a CV and one paragraph about why you think youβd be a good fit to rotskoff@stanford.edu
23.12.2024 17:31 β π 48 π 22 π¬ 0 π 0
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!
06.12.2024 19:53 β π 6 π 0 π¬ 0 π 0
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.
03.12.2024 17:16 β π 107 π 16 π¬ 1 π 1
NeurIPS Poster Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time ComplexityNeurIPS 2024
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...
03.12.2024 21:55 β π 9 π 2 π¬ 0 π 0
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
03.12.2024 14:58 β π 3 π 0 π¬ 0 π 0
A common question nowadays: Which is better, diffusion or flow matching? π€
Our answer: Theyβre two sides of the same coin. We wrote a blog post to show how diffusion models and Gaussian flow matching are equivalent. Thatβs great: It means you can use them interchangeably.
02.12.2024 18:45 β π 255 π 58 π¬ 7 π 7
Thanks, Kyle!
02.12.2024 16:13 β π 5 π 0 π¬ 0 π 0
Hello BlueSky! If and when I'm posting online, I'll be sure to do it here too.
21.11.2024 02:14 β π 4 π 0 π¬ 1 π 0
Assistant Professor at Dartmouth College
Computational Biophysics / Disordered Proteins / Molecular Recognition
Doing a Ph.D. AI in Bio. | Ex @WhiteLabGx @BroadInstitute @MIT | Built @PiPleteam | ML, Cancer, Genomics, Data Sci, Entrepreneur, FullStack Dev | All views are mine
1st year PhD Candidate Archimedes, Athena RC & NTUA
Assistant Professor of Machine Learning, Carnegie Mellon University (CMU)
Building a Natural Science of Intelligence π§ π€β¨
Prev: ICoN Postdoctoral Fellow @MIT, PhD @Stanford NeuroAILab
Personal Website: https://cs.cmu.edu/~anayebi
Assistant Professor of Neural Science at NYU & the Flatiron Institute.
Computational Neuroscience, Neural Networks, Neural Manifolds, Physics of Learning, & NeuroAI.
https://sites.google.com/site/sueyeonchung/
ML Research @ Apple.
Understanding deep learning (generalization, calibration, diffusion, etc).
preetum.nakkiran.org
Research Scientist @ Flatiron Institute, Simons Foudnation; PhD @ Princeton; Machine Learning for Scientific Computing since 2016. Opinions are my own.
Serra Group at UCSD Physics: Nonlinear Dynamics & Physics of Living Systems.
http://www.mattiaserra.com
Prev. |@SchmidtFellows @Harvard, PhD @ETH_en
Group Leader, Generative AI | NeurIPS 2024 Program Chair | Principal Scientist & Director | Founder of Amsterdam AI Solutions
Associate Professor of Machine Learning and Signal Processing, Technical University of Denmark (DTU)
https://frellsen.org
Assistant Professor at Mila and UdeM
https://necludov.github.io/
Post-doc @UniofOxford w/ @mmbronstein.bsky.social. Into Geometry β© Generative Models. @mila-quebec.bsky.social Affiliate member. Phd from @mila-quebec.bsky.social / McGill.
website: https://joeybose.github.io/
Building generative models for high-dimensional science and engineering.
Assistant prof. @CarnegieMellon & affiliated faculty @mldcmu, previously instructor @NYU_Courant, PhD jointly @Harvard and @MIT
https://nmboffi.github.io
Mathematician at Stanford
Statistician, Associate Professor (Lektor) at University of Gothenburg and Chalmers; inference and conditional distributions for anything
https://mschauer.github.io
http://orcid.org/0000-0003-3310-7915
[ΛmoΛ/r/Ιͺts ΛΚaΚΜ―Ι]
Searching for the numinous
Australian Canadian, currently living in the US
https://michaelnotebook.com
Blog: https://sander.ai/
π¦: https://x.com/sedielem
Research Scientist at Google DeepMind (WaveNet, Imagen 3, Veo, ...). I tweet about deep learning (research + software), music, generative models (personal account).
Official account for Harvard University. Devoted to excellence in teaching, learning, and research, and to developing leaders who make a difference globally.
Lecturer in Maths & Stats at Bristol. Interested in probabilistic + numerical computation, statistical modelling + inference. (he / him).
Homepage: https://sites.google.com/view/sp-monte-carlo
Seminar: https://sites.google.com/view/monte-carlo-semina
PhD candidate in applied math at Caltech interested in computational linear algebra he/him