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Daniel Gedon

@danigedon

PostDoc @uni_tue @mackelab 🇩🇪 | PhD @UU_University @WASP_Research 🇸🇪 | MSc @tudelft 🇳🇱 | Trying to understand DL by trying | @danielged.bsky.social 🦋
Tübingen, Deutschland dgedon.github.io Joined March 2019
868 Following 212 Followers 139 Posts

Latest posts 5

Daniel Gedon @danigedon
I co-wrote a blog post for the @AnthropicAI science blog. It has squids 🦑 and fruit flies 🪰 and AI helping to design scientific models.

@jakhmack @mackelab @ELLISInst_Tue @uni_tue
Anthropic Verified account @AnthropicAI 5d
New on our Science Blog: How AI is beginning to change scientific modeling.

Scientific models help researchers explain and predict real-world systems—like how a single nerve cell fires, or what happens when neutron stars collide. Recent advances have transformed how scientists fit complex models to data. But scientists still typically write the models manually, and usually need to weigh up several competing versions, each of which might explain the data in a different way.

In a guest post, Jakob Macke, Daniel Gedon, and Stefan Wahl describe how AI is starting to help with these parts of the process. By writing, running, fitting, and revising scientific models as code, AI can explore many possible model structures, and find out which explanations the data support.

anthropic.com/research/teach…
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Daniel Gedon @danigedon
Our @simtoscience workshop is coming to @NeurIPSConf in Paris this December 🇫🇷

Check out sim2science.com for paper submissions (deadline Aug 29 AoE) and details
www.sim2science.com NeurIPS 2026 Workshop Sim2Science: ML with Imperfect Scientific Models — NeurIPS 2026 Workshop
Sim2Science Workshop @simtoscience Jul 30
Announcing Sim2Science: ML with Imperfect Scientific Models @NeurIPSConf!

Submission deadline: 29 August 2026, 23:59 AoE

Workshop: 12 or 13 December 2026, Paris, France

5-page workshop papers & 2-page Tiny Papers via OpenReview
sim2science.com
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Daniel Gedon @danigedon
Check out all three NeurIPS papers from our lab! Cool stuff from simulating neural data to source distribution estimation.

Also, great work from my PhD group: papers on (1) generalizable policy evaluations from trial data and (2) entropy-regularized diffusion policies for RL.
Machine Learning in Science @mackelab Dec 9, 2024
Thrilled to announce we have three #NeurIPS2024 papers! Interested in simulating realistic neural data with diffusion models or recurrent neural networks, or in source distribution sorcery? Have a look 👇 1/4
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