Director, Center for Tech Responsibility@Brown. FAccT OG. AI Bill of Rights coauthor. Former tech advisor to President Biden @WHOSTP. He/him/his. Posts my own.
Anti-cynic. Towards a weirder future. Reinforcement Learning, Autonomous Vehicles, transportation systems, the works. Asst. Prof at NYU
https://emerge-lab.github.io
https://www.admonymous.co/eugenevinitsky
Machine Learning, HAI, Visualization, Data Tooling
Machine Learning Student @ KTH
Interested in all things bach, piano, outdoors, data, cooking.
Human/AI interaction. ML interpretability. Visualization as design, science, art. Professor at Harvard, and part-time at Google DeepMind.
Research @carnegiemellon. || Building Causal Agent and ArbitrageX ||
Interested in reinforcement learning, alignment, birds, jazz music ||
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
PhD Student at @gronlp.bsky.social 🐮, core dev @inseq.org. Interpretability ∩ HCI ∩ #NLProc.
gsarti.com
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/
