1/ Privacy-preserving credentials can prove you're over 18, but not much else. Our new research changes that. 𝜋Creds introduces verifiable credentials generated by trusted LLM inference over authenticated.

Deploying algorithmic research in practice is an opaque process. We're organizing a workshop at EC to share behind-the-scenes stories and move the field foward. Call for submissions open! With @nkgarg.bsky.social @ericachiang.bsky.social, Bailey Flanigan sites.google.com/cornell.edu/...

We are very excited to announce our first workshop on From Theory to Practice: behind the scenes on research deployments at EC’26 (July 6 in Rome)! Call for posters and submissions now open! Organized by myself, @ericachiang.bsky.social , Bailey Flanigan, @brwilder.bsky.social

Excited to share this work, led by Kenny, on our vision for using language models to bring back a more exploratory internet experience. New essay and demo linked in Kenny’s thread :)

Excited to share our new research demo, where you can freely traverse the world of Bluesky through 20,000 interconnected trails, spanning “analysis of fictional tropes” to “rotisserie chicken” to “zoning and land use policy.” Try it out, and let us know what you think!

New in Nature Health: how might we move towards a world in which race is not used in clinical algorithms? We need (1) careful comparison of race-aware and race-neutral algorithms and (2) systemic efforts to address underlying disparities.

New paper! The Linear Representation Hypothesis is a powerful intuition for how language models work, but lacks formalization. We give a mathematical framework in which we can ask and answer a basic question: how many features can be stored under the hypothesis? 🧵 arxiv.org/abs/2602.11246

Excited to present a new preprint with @nkgarg.bsky.social: presenting usage statistics and observational findings from Paper Skygest in the first six months of deployment! 🎉📜 arxiv.org/abs/2601.04253

New #NeurIPS2025 paper: how should we evaluate machine learning models without a large, labeled dataset? We introduce Semi-Supervised Model Evaluation (SSME), which uses labeled and unlabeled data to estimate performance! We find SSME is far more accurate than standard methods.

selfishly i wish we could keep divya in our lab forever but i guess it would be a disservice to the rest of the world 😅 she’s been such a wonderful mentor to me—i’ve learned a lot from how thoughtful, creative, and knowledgeable she is about everything. she’s also super funny and amazing at baking 🤭

I can’t believe I’m saying this: our work received a Best Paper Award at #CHIL2025!! So so excited and grateful 🥰 Looking forward to day 2 of the conference with these awesome people :)

I wrote about science cuts and my family's immigration story as part of The McClintock Letters organized by @cornellasap.bsky.social. Haven't yet placed it in a Houston-based newspaper but hopefully it's useful here gargnikhil.com/posts/202506...

New work 🎉: conformal classifiers return sets of classes for each example, with a probabilistic guarantee the true class is included. But these sets can be too large to be useful. In our #CVPR2025 paper, we propose a method to make them more compact without sacrificing coverage.

I’m really excited to share the first paper of my PhD, “Learning Disease Progression Models That Capture Health Disparities” (accepted at #CHIL2025)! ✨ 1/ 📄: arxiv.org/abs/2412.16406

The US government recently flagged my scientific grant in its "woke DEI database". Many people have asked me what I will do. My answer today in Nature. We will not be cowed. We will keep using AI to build a fairer, healthier world. www.nature.com/articles/d41...

check out the findings from our #dogathon 😍🐶 !!

Migration data lets us study responses to environmental disasters, social change patterns, policy impacts, etc. But public data is too coarse, obscuring these important phenomena! We build MIGRATE: a dataset of yearly flows between 47 billion pairs of US Census Block Groups. 1/5

Excited to announce a new preprint from my lab (with @rishi-jha.bsky.social and Vitaly Shmatikov; my first as a first author!) about severe security vulnerabilities in LLM-based multi-agent systems: “Multi-Agent Systems Execute Arbitrary Malicious Code” arxiv.org/abs/2503.12188 1/12

(1/n) New paper/code! Sparse Autoencoders for Hypothesis Generation HypotheSAEs generates interpretable features of text data that predict a target variable: What features predict clicks from headlines / party from congressional speech / rating from Yelp review? arxiv.org/abs/2502.04382

💡New preprint & Python package: We use sparse autoencoders to generate hypotheses from large text datasets. Our method, HypotheSAEs, produces interpretable text features that predict a target variable, e.g. features in news headlines that predict engagement. 🧵1/

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...

I'm excited to use my first post here to introduce the first paper of my PhD, "User-item fairness tradeoffs in recommendations" (NeurIPS 2024)! This is joint work with Sudalakshmee Chiniah and my advisor @nkgarg.bsky.social Description/links below: 1/