Could Christiano and Messi get along? Dennis and Melissa sure can, in an ATLAS-CMS collaborative effort to explain anomaly detection ;)

Today Sofia Palacios Schweitzer introduces GANs as 'early attempts' in her generative models lecture ...👴

And we're off! The Machine Learning for Fundamental Physics School 2026 starts at @gtsciences.bsky.social. Melissa Quinnan introduces foundational concepts to a room full of eager learners

Such fun stories, from understanding sheep brain damage to searching for dark matter. My favourite was reading about moments when you find out something that the rest of the world doesn't know yet.

Science communication people! Come tell us about your experience at the APS DPF meeting! Submit your abstract by the May 11 deadline: indico.fnal.gov/event/72820/...

The Machine Learning for Fundamental Physics School is back! This time bigger and in a new location: Georgia Institute of Technology (@gtsciences.bsky.social)! We can cover the domestic travel and accommodation of most students and ECRs participating. Apply soon! indico.global/event/17000/

Our two new research areas are now live!

📢 𝐆𝐫𝐚𝐝 𝐚𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧𝐬 𝐨𝐩𝐞𝐧, 𝐞𝐚𝐫𝐥𝐢𝐞𝐫 𝐝𝐞𝐚𝐝𝐥𝐢𝐧𝐞 𝐭𝐡𝐢𝐬 𝐲𝐞𝐚𝐫!
 Physics at 𝐆𝐞𝐨𝐫𝐠𝐢𝐚 𝐓𝐞𝐜𝐡 is accepting applications. 𝐌𝐲 𝐠𝐫𝐨𝐮𝐩 𝐨𝐧 𝐀𝐈 ∩ 𝐩𝐡𝐲𝐬𝐢𝐜𝐬 𝐢𝐬 𝐠𝐫𝐨𝐰𝐢𝐧𝐠! So if you know students interested in 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥 𝐩𝐡𝐲𝐬𝐢𝐜𝐬 𝐨𝐫 𝐚𝐬𝐭𝐫𝐨, please share this.
 🗓️ Deadline: 𝐃𝐞𝐜𝐞𝐦𝐛𝐞𝐫 𝟏𝟓
 
🔗 physics.gatech.edu/academics/gr...

📢𝐉𝐨𝐛 𝐚𝐥𝐞𝐫𝐭: Postdoc position(s) in 𝐀𝐈 𝐟𝐨𝐫 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥 𝐩𝐡𝐲𝐬𝐢𝐜𝐬 focusing on either 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 or 𝐀𝐈-𝐚𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐭𝐡𝐞𝐨𝐫𝐲. 🏙️Join my new group at 𝐆𝐞𝐨𝐫𝐠𝐢𝐚 𝐓𝐞𝐜𝐡, based in 𝐰𝐨𝐧𝐝𝐞𝐫𝐟𝐮𝐥 𝐌𝐢𝐝𝐭𝐨𝐰𝐧 𝐀𝐭𝐥𝐚𝐧𝐭𝐚! 🗓️ Reviews begin sooner, deadline: 𝟓𝐭𝐡 𝐃𝐞𝐜𝐞𝐦𝐛𝐞𝐫 💬 Questions: Reach out! 🔗 inspirehep.net/jobs/2962185

🚨 Job Alert We're hiring our first Postdoc to work with us on our @erc.europa.eu Project on Generative AI for Particle Physics. Check out the details below and don't hesitate to get in touch or spread the word! Deadline: Nov 30th inspirehep.net/jobs/3075448

Setting up my research group Slack workspace (free version). Has anyone considered Discord / Zulip / something else? @marielpettee.bsky.social @lukasheinrich.com

Yeah, even crazier- last year for my postdoc ad a full half of the applications legit never made reference to anything my lab does and how my research would connect to theirs. You are literally in the top half of applicants for just TRYING to draw a connection in a sentence in the cover letter!

Like AI + Physics, which is a well-developed field but we're still only scratching the surface of what we can do!

At the launch event for ‪@gtsciences.bsky.social‬'s AI4Science Centre: ai4science.ai.gatech.edu. With colleagues from psychology, math, earth sciences and everything in between. Looking forward to all the cross-pollination within the sciences and also with industry!

A little comment like one of these makes my week. Teaching and mentorship is easily the most rewarding aspect of academia. Makes it all worth it. Thanks again to all the students who came to ML4FP and interacted with us for a week. Your positive comments mean the word to us!

Today we have the ATLAS, CMS and Neutrino parallel sessions at the ML4FP School. Julia Gonski give us an overview of ML in ATLAS

GPT5's explanation for FTS is quite impressive, especially where it gives an intuitive explanation that is not just directly lifted from the paper. Makes it easy to learn a new statistics concept. chatgpt.com/s/t_6894ece0...

And we're off! Yifan Chen welcomes students to the Machine Learning for Fundamental Physics School 2025!

What if you could beat the Likelihood Ratio Test? A fun and potentially transformational phy-statistics paper with Ann Lee, James Carzon, @rafael-izbicki.bsky.social, luca Masserano and @danielwhiteson.bsky.social arxiv.org/abs/2507.17831

Happy this paper is finally on arxiv! A fun mix of physics (jet substructure) being used to calibrate the performance of ML algorithms (jet taggers) to get proper uncertainties Now a standard CMS tool for interpreting searches for exotic jets, used in multiple analyses

New paper: Constraining the Higgs self-coupling from the off-shell single Higgs with NSBI. Work with Tae Hyoun Park, Maximilian Griese & Ulrich Haisch. arxiv.org/abs/2507.02032 

So happy to see @aishikghosh.bsky.social thrive - he’s been one of the drivers of AI in HEP and I can’t wait to see his lab start up - take note of the postdoc position coming up.

Some happy news — I’ve accepted a tenure-track faculty position in the School of Physics at Georgia Tech! My group will work at the intersection of AI and fundamental physics & astrophysics. GT is making massive investments in these directions, and I’m hiring a postdoc — more soon!

Delighted to see our work featured in the news! Two key players mentioned but unfortunately not explicitly named: discussions with Kyle Cranmer and Johann Brehmer were crucial in the initial stages. Also Jay, Rafael were central in making it a reality in ATLAS.

Our work on simulation-based inference at the LHC is featured in @arstechnica.com! Well done @aishikghosh.bsky.social @sandesarajay.bsky.social 🙌 arstechnica.com/science/2025...

Wondered what it would take for AI agents to design and test new theories for fundamental physics, just like a physicist? We show how, an example in neutrino model building! Paper with Jake, Max, Victoria, Jason leading crucial aspects, also @danielwhiteson.bsky.social . arxiv.org/abs/2506.08080

Breakthrough Prize awarded to LHC experiments for the study of Higgs properties (to which I contributed), rare processes, matter-antimatter asymmetry, and many other areas of research

New paper! Learning Broken Symmetries with Approximate Invariance Led by HIGH SCHOOL STUDENT Seth Nabat, with Aishik_Ghosh, Ed Witkowski and GregorKasieczka. arxiv.org/abs/2412.18773

We show that a hybrid (conserving+breaking) architecture combines the advantages of symmetry-conserving networks (fast learning) and breaking ones (higher maximum performance). Great work by Seth with @aishikghosh.bsky.social, Ed, @danielwhiteson.bsky.social Full paper: arxiv.org/abs/2412.18773

Now(ish) on arxiv: Learning Broken Symmetries with Approximate Invariance Invariances (or, as physicists call them: symmetries) of the data can be baked into a ML model to improve performance or data efficiency. However, in reality, these symmetries are often broken. How to deal with that?

DSI Research Scientist @matthewfeickert.com focuses on data science applications and #opensource software for particle physics. Read his story: ospo.wisc.edu/community/pr...

It’s a pleasure to host @aishikghosh.bsky.social in Munich, where he he’s sharing the NSBI story that he has pushed in @atlasexperiment.bsky.social for many years and recently came out

The propaganda slide about how the next discovery may be made in particle physics, from my CERN seminar: indico.cern.ch/event/1479992/

Simulation-based inference in ATLAS! 👀 Very proud to have contributed to this project, from its inception 10 years ago, to its publication this morning. NSBI replaces histogram-based analysis with a per-event analysis based likelihood-ratios modeled by NNs. Gains in sensitivity are substantial!

First post on BlueSky! I'm excited to give a seminar at #CERN tomorrow on 6 years of work developing a complete statistical framework for high-dimensional inference of the Higgs width. Simulation-based inference on actual collision data in ATLAS, profiling all systematic uncertainties!