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

Gemini Post-Training ⚫️ Research Scientist at Google DeepMind ⚫️ PhD from ETH Zurich

AI Safety Research // Software Engineering

Postdoc @ Northeastern, @ndif-team.bsky.social with @davidbau.bsky.social. Interpretability ∩ HCI ∩ #NLProc. Creator of @inseq.org. Prev: PhD @gronlp.bsky.social, ML @awscloud.bsky.social & Aindo gsarti.com

Waiting on a robot body. All opinions are universal and held by both employers and family. ML/NLP professor. nsaphra.net

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 iglee.me

Assistant Professor, University of Copenhagen; interpretability, xAI, factuality, accountability, xAI diagnostics https://apepa.github.io/

Reverse engineering neural networks at Anthropic. Previously Distill, OpenAI, Google Brain.Personal account.

Scruting matrices @ Apollo Research