*Please repost* @sjgreenwood.bsky.social and I just launched a new personalized feed (*please pin*) that we hope will become a "must use" for #academicsky. The feed shows posts about papers filtered by *your* follower network. It's become my default Bluesky experience bsky.app/profile/pape...
10.03.2025 18:14 — 👍 509 🔁 293 💬 24 📌 79
Please repost to get the word out! @nkgarg.bsky.social and I are excited to present a personalized feed for academics! It shows posts about papers from accounts you’re following bsky.app/profile/pape...
10.03.2025 15:12 — 👍 125 🔁 87 💬 6 📌 11
Presenting our work on empirical aspects of multicalibration at neurips today:
11am West Ballroom A-D #5803
Here's the paper: arxiv.org/abs/2406.06487. See you around!
12.12.2024 17:46 — 👍 5 🔁 0 💬 1 📌 0
how many cards do you have in your anki deck 5 years later?
21.11.2024 21:32 — 👍 0 🔁 0 💬 1 📌 0
CS PhD student at Cornell Tech. Interested in interactions between algorithms and society. Princeton math '22.
kennypeng.me
https://tianyi-lorena-yan-me.web.app/
The world's leading venue for collaborative research in theoretical computer science. Follow us at http://YouTube.com/SimonsInstitute.
Interpretable Deep Networks. http://baulab.info/ @davidbau
Algorithms, predictions, privacy.
https://theory.stanford.edu/~sergei/
Ph.D. candidate @UMich. NSF Graduate Research Fellow @NSFGRFP and previous intern @Apple
My website: https://vinodkraman.github.io
ELLIS & IMPRS-IS PhD Student at the University of Tübingen.
Excited about uncertainty quantification, weight spaces, and deep learning theory.
Assistant Prof at Penn CIS | Postdoc at Microsoft Research | PhD from UT Austin CS | Co-founder LeT-All
PhD student at UPenn, in machine learning / responsible AI and game theory.
guptavarun.com
PhD from NUS; All things privacy
Lead Privacy Engineer
https://comp.nus.edu.sg/~rishav1
PhD candidate @UMD | Responsible AI
Chief scientist at Robust Intelligence and professor at Yale (on leave)
full-time ML theory nerd, part-time AI-non enthusiast
Principal Researcher in AI/ML/RL Theory @ Microsoft Research NE/NYC. Previously @ MIT, Cornell. http://dylanfoster.net
RL Theory Lecture Notes: https://arxiv.org/abs/2312.16730
Sr Research Scientist at Google DeepMind, Toronto. Member, Mila. Adjunct, McGill CS. PhD Machine Learning & MASt Applied Math (Cambridge), BSc Math (Warwick). gkdz.org
Computer science professor at Carnegie Mellon. Researcher in machine learning. Algorithmic foundations of responsible AI (e.g., privacy, uncertainty quantification), interactive learning (e.g., RLHF).
https://zstevenwu.com/
CS PhD student at UPenn studying strategic human-AI interaction. On the job market! Nataliecollina.com
wharton stats phd — ml theory, ml for science
prev: comp neuro, data, physics
working with Edgar Dobriban and Konrad Körding
also some sports (esp. philly! go birds)
cs phd @upenn advised by Michael Kearns, Aaron Roth, and Duncan Watts| previously @stanford | she/her
https://psamathe50.github.io/sikatasengupta/
PhD student at the University of Pennsylvania. Prev, intern at MSR, currently at Meta FAIR. Interested in reliable and replicable reinforcement learning, robotics and knowledge discovery: https://marcelhussing.github.io/
All posts are my own.
