Former PhD @ MIT CSAIL. Finding/exploiting patterns in data, especially symmetries. AI4Science. https://xitter.cf/t.co/0XcSE5V8S2. Cofounder @bostonsymmetry.
Joined April 2021
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@bostonsymmetry is hosting a poster session (4-5:30pm) + social on Tuesday, June 9th, at Northeastern! All are welcome to come chat about geometry, symmetries, + AI!
Location: Raytheon Amphitheater, maps.app.goo.gl/oDZc3Rw7iN4q…
Register here: neu.co1.qualtrics.com/jfe/fo…
Hannah Lawrence retweeted
Starting today, 10,000 scientists across every field, from math to chemistry to physics and more, can get Claude through our new Claude Team plan for scientists. Standard seats are free, and premium seats with 5x usage limits are $15 per month, an 80% discount, for one year.
Claude is becoming increasingly capable of scientific work, with recent progress on problems from advanced physics calculations to protein design. Alongside that progress, we've been investing in the research community: Claude Science launched in June, and our AI for Science program funds high-impact projects with free credits. Today's expansion builds on both.
Principal investigators (or equivalent) at academic and nonprofit research institutions can sign up, then add the researchers in their group. Over the coming months, we plan to extend the program well beyond the initial 10,000 seats.
Learn more: claude.com/programs/team-pla…
Hannah Lawrence retweeted
What is scientific taste?
Tell us what you think in this 10-min Claude-led interview on how scientists choose problems, judge evidence, and decide what's worth doing.
claude.ai/interviewer/how_sc…
Hannah Lawrence retweeted
Just under two weeks until the submission deadline for the Boston TAG Party 2026– a joint conference organized collaboratively by the Boston Symmetry Group and TAG-DS! Papers are due June 12th— full archival papers, extended abstracts, and open problem tracks!
Hannah Lawrence retweeted
Results for the GRaM Competition are online. Congratulations to the winner and runners-up 🎉
🥇 github.com/julka01 with SmoothSplatNet
🥈 github.com/jorgesarrato with CDFDoubleGridNet
🥉 github.com/v3ctr0id with VRTEnsemble
gram-competition.github.io
Hannah Lawrence retweeted
A memorable final quote from @AlexanderTong7 (echoed by several of the panelists): "geometry is about understanding the world, and it is here to stay."
Hannah Lawrence retweeted
Next up: @mayabechlerspei is sharing her insights on billion-parameter graph foundation models!
Hannah Lawrence retweeted
Unmissable panel starting now! @wellingmax, @mmbronstein, Kathlén Kohn, Gabriel Loaiza-Ganem, and @AlexanderTong7 discuss "scale, simplicity, and geometry", moderated by @erikjbekkers
The first question: is scale really all you need? What is the role of inductive bias? 🌶️
Hannah Lawrence retweeted
Will be speaking today at the GRaM workshop at ICLR @iclr_conf (gram-workshop.Github.io/ ) about how we are building efficient and effective billion-scale Graph Foundation Models at Meta!!! 🤩
(GraphBFF 👉 lnkd.in/dB67TZN6).
Anddddd we also have an awesome paper at the workshop! "Improving LLM Predictions via Inter-Layer Structural Encoders", poster session A :) (lnkd.in/dZCnztY5)
#iclr2026
Hannah Lawrence retweeted
Gabriel Loaiza-Ganem is speaking NOW on "A Geometric View of Deep Generative Models"!
The claim: generative models can be divided into (data) manifold-aware and manifold-unaware methods. Manifold-aware methods work better!
Hannah Lawrence retweeted
GRaM 2026 is kicking off NOW in 101A with @erikjbekkers!
Full schedule at gram-workshop.github.io/ #doitfortheGRaM
Hannah Lawrence retweeted
We're excited to kick off the GRaM Workshop tomorrow (Sunday) at 9am in Room 101A! Check out the paper search tool on our website, where you can find posters at the intersection of your interests :)
Full schedule + speakers at gram-workshop.github.io
#doitfortheGRaM
Was the whole blogpost an excuse to make this meme? Perhaps.
We must choose both the tokens AND their order.
A joint distribution can be factored into a product of conditionals in any order, but this choice determines the available context at each step. Thus, some orders are easier to model in practice (arxiv.org/abs/2502.06768)
(2/7)
Come chat about tokenization and trends in the treatment of non-sequential data!
P4-5313
Hannah Lawrence retweeted
You have a safe model you've tested, and you have a new post-trained model. How far can you trust a new model before it becomes unsafe? The answer goes to the heart of statistical decision-making. To be safe, the agent must be self-aware. Read @DrewPrinster's thread for more.
Can we ensure AI agents respect our safety constraints, even as they explore & improve?
- Medical LLMs that are helpful, & avoid false claims?
- Bioscience agents that generate effective molecule designs, & ensure they’re safe?
📄🧵w/ @samuel_stanton_ @clara_fannjiang @jiwoncpark @kchonyc @anqi_liu33 @suchisaria
Excited to share “Conformal Policy Control” ⬇️
1/12
TL;DR: poster today at 3:15pm, P3-#1109!
Have you ever benchmarked your method on QM9, MD17, OC20, or ModelNet? It turns out that the 3D orientations of point clouds in commonly used datasets are highly non-random. How did we prove this and why should you care? 🧵
So: where does this leave equivariant methods? Here’s a handy flowchart :)
(N.B. we’re lumping together equivariant architectures and augmentation here. They are of course different, and our experiments used both. But both provably discard input-level rotational biases!)
It was a pleasure working on this with co-first author @ElyssaHofgard and great collaborators @vportilheiro, Yuxuan Chen, @tesssmidt, and @RobinSFWalters. Come chat with Elyssa, Vasco, and I this afternoon!
Paper: arxiv.org/pdf/2510.01349
Blog post: ehofgard.github.io/blog/2026…