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

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