Mechanistic interpretability
Creator of https://github.com/amakelov/mandala
prev. Harvard/MIT
machine learning, theoretical computer science, competition math.
research scientist @deepmind. language & multi-agent rl & interpretability. phd @BrownUniversity '22 under ellie pavlick (she/her)
https://roma-patel.github.io
I make sure that OpenAI et al. aren't the only people who are able to study large scale AI systems.
Physics, Visualization and AI PhD @ Harvard | Embedding visualization and LLM interpretability | Love pretty visuals, math, physics and pets | Currently into manifolds
Wanna meet and chat? Book a meeting here: https://zcal.co/shivam-raval
Interpretability researcher at @eleutherai.bsky.social
PhD Student at the ILLC / UvA doing work at the intersection of (mechanistic) interpretability and cognitive science. Current Anthropic Fellow.
hannamw.github.io
Independent Mechanistic Interpretability Researcher
✨ mechanistic interpretability research scientist @ Goodfire | deep learning, math, biology | creating a more beautiful future
sonospheric communard // central spectroid // lecturer in musicology @ salford uni // timbre, genre, electronic music, cognition, digital humanities, mycelia
Interpretable Deep Networks. http://baulab.info/ @davidbau
Stanford Linguistics and Computer Science. Director, Stanford AI Lab. Founder of @stanfordnlp.bsky.social . #NLP https://nlp.stanford.edu/~manning/
Human/AI interaction. ML interpretability. Visualization as design, science, art. Professor at Harvard, and part-time at Google DeepMind.
Scruting matrices @ Apollo Research
https://mega002.github.io
I train models @ OpenAI.
Previously Research at DeepMind.
Hae sententiae verbaque mihi soli sunt.
Master student at ENS Paris-Saclay / aspiring AI safety researcher / improviser
Prev research intern @ EPFL w/ wendlerc.bsky.social and Robert West
MATS Winter 7.0 Scholar w/ neelnanda.bsky.social
https://butanium.github.io
Aspiring 10x reverse engineer at Google DeepMind
AI safety at Anthropic, on leave from a faculty job at NYU.
Views not employers'.
I think you should join Giving What We Can.
cims.nyu.edu/~sbowman
