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
Postdoc at Northeastern and incoming Asst. Prof. at Boston U. Working on NLP, interpretability, causality. Previously: JHU, Meta, AWS
Interpretable Deep Networks. http://baulab.info/ @davidbau
https://mega002.github.io
AI Safety Research // Software Engineering
PhD Student at @gronlp.bsky.social 🐮, core dev @inseq.org. Interpretability ∩ HCI ∩ #NLProc.
gsarti.com
Waiting on a robot body. All opinions are universal and held by both employers and family.
Literally a professor. Recruiting students to start my lab.
ML/NLP/they/she.
Machine learning haruspex
NLP PhD student at Imperial College London and Apple AI/ML Scholar.
Machine learning PhD student @ Blei Lab in Columbia University
Working in mechanistic interpretability, nlp, causal inference, and probabilistic modeling!
Previously at Meta for ~3 years on the Bayesian Modeling & Generative AI teams.
🔗 www.sweta.dev
Machine Learning PhD Student
@ Blei Lab & Columbia University.
Working on probabilistic ML | uncertainty quantification | LLM interpretability.
Excited about everything ML, AI and engineering!
PhD student at Vector Institute / University of Toronto. Building tools to study neural nets and find out what they know. He/him.
www.danieldjohnson.com
Mechanistic interpretability
Creator of https://github.com/amakelov/mandala
prev. Harvard/MIT
machine learning, theoretical computer science, competition math.
Post-doc @ Harvard. PhD UMich. Spent time at FAIR and MSR. ML/NLP/Interpretability
Computer Science PhD student | AI interpretability | Vision + Language | Cogntive Science. Prev. intern @MicrosoftResearch.
https://martinagvilas.github.io/
ml/nlp phding @ usc, currently visiting harvard, scientisting @ startup;
interpretability & training & reasoning & ai for physics
한american, she, (slightly outdated) iglee.me
Assistant Professor, University of Copenhagen; interpretability, xAI, factuality, accountability, xAI diagnostics https://apepa.github.io/
Computation & Complexity | AI Interpretability | Meta-theory | Computational Cognitive Science
https://fedeadolfi.github.io
Reverse engineering neural networks at Anthropic. Previously Distill, OpenAI, Google Brain.Personal account.
Scruting matrices @ Apollo Research
