iiinova — where your digital self keeps evolving

To address the risk of congestion from nudging too many students to a given program, we developed a method for recommendation in equilibrium. We then deployed the recommendations as part of a randomized controlled trial with NYC Public Schools. More details soon once the full paper is out! (4/5)

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Building on this work, we partnered with NYC Public Schools to send personalized recommendations to students from target middle schools, nudging them to nearby, high-performing high school programs where they have a high chance of admission. (3/5)

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Work led by @kennypeng.bsky.social and Emily Ryu found disparities in application behavior and outcomes among students applying to high school in NYC, motivating the need for interventions that decrease information disparities. (2/5) bsky.app/profile/kenn...

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https://news.cornell.edu/stories/2026/08/research-helps-nyc-students-aim-higher-public-high-school-applications

Thanks to Grace Stanley for covering our work :) Our paper on personalized recommendations for students applying to NYC high schools (best student paper at EC ’26(!!)) is coming soon! Here's a quick preview... (1/5) news.cornell.edu/stories/2026...

t.co

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danivilardell.bsky.social @danivilardell.bsky.social · 03/06/2026

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.

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Bryan Wilder @brwilder.bsky.social · 11/05/2026

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/...

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About This workshop will focus on the practical realities of deploying algorithmic and economic systems from academic research, especially with government and non-profit partners. While economics and ...

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Nikhil Garg @nkgarg.bsky.social · 11/05/2026

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

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Erica Chiang @ericachiang.bsky.social · 15/04/2026

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 :)

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Kenny Peng @kennypeng.bsky.social · 26/03/2026

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!

skytrails.org

skytrails · 20,000 trails through Bluesky

Can we regain freedom of movement on social media? Browse Bluesky via interconnected trails.

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Divya Shanmugam @dmshanmugam.bsky.social · 23/03/2026

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.

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Kenny Peng @kennypeng.bsky.social · 17/02/2026

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

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Sophie Greenwood @sjgreenwood.bsky.social · 14/01/2026

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

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Divya Shanmugam @dmshanmugam.bsky.social · 17/10/2025

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.

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Erica Chiang @ericachiang.bsky.social · 14/10/2025

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 🤭

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Erica Chiang @ericachiang.bsky.social · 04/07/2025

CONGRATS this is so exciting!!!

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Erica Chiang @ericachiang.bsky.social · 03/07/2025

aww thank you!!! you too for your best paper 😌🫶🏼

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Erica Chiang @ericachiang.bsky.social · 27/06/2025

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 :)

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Nikhil Garg @nkgarg.bsky.social · 16/06/2025

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...

gargnikhil.com

Science and immigration cuts · Nikhil Garg

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Divya Shanmugam @dmshanmugam.bsky.social · 14/06/2025

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.

A gif explaining the value of test-time augmentation to conformal classification. The video begins with an illustration of TTA reducing the size of the predicted set of classes for a dog image, and goes on to explain that this is because TTA promotes the true class's predicted probability to be higher, even when it's predicted to be unlikely.

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I really enjoyed (and learned a LOT from) working on this project with these wonderful co-authors: @dmshanmugam.bsky.social Ashley Beecy Gabriel Sayer @destrin.bsky.social @nkgarg.bsky.social @emmapierson.bsky.social 7/7

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Our work underscores the importance of accounting for health disparities; we lay a foundation for doing so with a method to (1) estimate disease severity in the presence of health disparities and (2) identify disparity patterns that can inform public health interventions. 6/

The interpretability and identifiability of our model also allow us to learn fine-grained descriptions of disparities. Fitting our model on heart failure patient data from NewYork-Presbyterian, our model identifies groups that face each type of health disparity. 5/

We prove that *failing to* account for these disparities biases severity estimates. By jointly accounting for all three, our model more accurately recovers severity. Indeed, accounting for these disparities in real heart failure data does meaningfully shift severity estimates. 4/

We propose an interpretable disease progression model that captures 3 key disparities: certain patient groups may (1) start receiving care at higher disease severity levels, (2) experience faster disease progression, or (3) receive less frequent care conditional on severity. 3/

Disease progression models are often used to help healthcare providers diagnose and treat chronic diseases. But these models have historically failed to account for health disparities that bias the data they are trained on. 2/