Siddharth Mishra-Sharma

🎉 Great news: Our Machine Learning and Physical Sciences workshop will be back again this year! 🎉
Keep an eye out for updates on deadlines etc, we will be updating the website soon
ml4physicalsciences.github.io
#ML4PS2025

Inside the Secret Meeting Where Mathematicians Struggled to Outsmart AI The world's leading mathematicians were stunned by how adept artificial intelligence is at doing their jobs

www.scientificamerican.com/article/insi...

Implying there was a point before

Congrats and welcome!!

Rediscovering One of the Institute's First Women of Color Since 1933, the Institute has welcomed over 8,000 Members comprising scholars from nearly every part of the globe. For many of these scholars, the Shelby White and Leon Levy Archives Center holds some...

www.ias.edu/ideas/redisc...

Are you using the API or Console (console.anthropic.com) to get more Claude? (Happy to pass along any feedback...)

2024 letter

zhengdongwang.com/2024/12/29/2...

📣 Hiring! I am looking for PhD/postdoc candidates to work on foundation models for science at @ULiege, with a special focus on weather and climate systems. 🌏 Three positions are open around deep learning, physics-informed FMs and inverse problems with FMs.

Yah agreed! Opportunity cost aside, I think a generally nice time as a postdoc, then going to industry seems good from a happiness perspective. It seems likely that regret here mostly reflects a less-than-ideal experience post-PhD (by default for systemic reasons).

(If true) I think that's a very specific case, I think industry employers generally don't care or might even weakly prefer fewer years post-PhD

I remain a contrarian on this!

Got a photo with a nice branch of my academic family tree: @awehwe.bsky.social @glouppe.bsky.social @smsharma.bsky.social @lukasheinrich.com
Nicole Hartman
@annalenakofler.bsky.social

The papers and posters for our Machine Learning and Physical Sciences workshop at #NeurIPS2024 are online #ml4ps2024. Come check it out on Sunday
ml4physicalsciences.github.io/2024/

Poster 4.15, talk 2.30! (Is the schedule shown differently somewhere?)

Looking forward to catching up with friends and colleagues at NeurIPS next week, as well as meeting new folks -- feel free to reach out ☕!

One hypothesis is that the explicit coarse-graining hurts expressivity, which is preserved when modeling long-range correlations implicitly via the vanilla MPGNN. But not sure -- maybe it's possible to set-up a PointNet-style model to do better!

Yep, there are long range correlations in the data, and the global parameter $\sigma_8$ should be especially sensitive to them. Indeed PointNet++ doesn't seem to do very well at capturing them despite explicitly having hierarchical downsampling.

are we getting the greatest hits

A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing Efficiently processing structured point cloud data while preserving multiscale information is a key challenge across domains, from graphics to atomistic modeling. Using a curated dataset of simulated ...

Fun paper led by Julia Balla: "A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing".

Julia will be presenting the paper at LoG on Thursday as a spotlight oral, and also at the NeurReps Workshop at NeurIPS Workshop next month.

📄: arxiv.org/abs/2410.20516
💻: github.com/smsharma/eqn...

Would like to join!

blue sky