iiinova — where your digital self keeps evolving

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Ira Globus-Harris @iraglobusharris.bsky.social · 03/07/2026

Are you at ICML next week? Feel like your decision-making for which sessions to attend might not be risk minimizing? Don't incur (swap) regret and come to my, @aaroth.bsky.social, and @ncollina.bsky.social's tutorial Monday on multicalibration, decision-making, and collaborative learning!

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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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Gabriel Agostini @gsagostini.bsky.social · 05/02/2026

We found, for example, racial disparities in upward mobility —that is, the rate at which people move to higher-income areas varies according to the racial composition of their current area of residence, even after controlling for income levels. 6/9

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Gabriel Agostini @gsagostini.bsky.social · 05/02/2026

Our paper “Inferring fine-grained migration patterns across the United States” is now out in @natcomms.nature.com! We released a new, highly granular migration dataset. 1/9

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Maria Antoniak @mariaa.bsky.social · 29/01/2026

CS ArXiv recently banned “review and position” papers, but what are those? Do they include more generated content? Who is most affected by this change? @yanai.bsky.social and I dug into the data to find out! Nearly 50% of Computers & Society papers might be censored, vs 3% of Computer Vision ‼️

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David Liu @david-m-liu.bsky.social · 28/01/2026

🎙️ I had a great time joining the Data Skeptic podcast to talk about my work on recommender systems If you're interested in embeddings, aligning group preferences, or music recommendations, check out the episode below 👇 open.spotify.com/episode/6IsP...

open.spotify.com

Fairness in PCA-Based Recommenders

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Shuvom Sadhuka @shuvoms.bsky.social · 18/11/2025

I’m excited to share our new paper A Bayesian Model for Multi-stage Censoring, which I will present at #ML4H2025 in San Diego! 🧵 below:

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Jessica Hullman @jessicahullman.bsky.social · 05/11/2025

🧠⚙️ Interested in decision theory+cogsci meets AI? Want to create methods for rigorously designing & evaluating human-AI workflows? I'm recruiting PhDs to work on: 🎯 Stat foundations of multi-agent collaboration 🌫️ Model uncertainty & meta-cognition 🔎 Interpretability 💬 LLMs in behavioral science

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Kate Donahue @kpaxdonahue.bsky.social · 06/11/2025

I’m recruiting students this upcoming cycle at UIUC! I’m excited about Qs on societal impact of AI, especially human-AI collaboration, multi-agent interactions, incentives in data sharing, and AI policy/regulation (all from both a theoretical and applied lens). Apply through CS & select my name!

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Divya Shanmugam @dmshanmugam.bsky.social · 06/11/2025

if you think about AI, healthcare, women's health, or all of the above, i highly recommend this article on the role of fetal heart rate monitors in the rise of C-sections: www.nytimes.com/2025/11/06/h...

nytimes.com

The ‘Worst Test in Medicine’ is Driving America’s High C-Section Rate

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Divya Shanmugam @dmshanmugam.bsky.social · 03/11/2025

Super cool, and something I wish existed within machine learning for healthcare too! I'm often wondering what people are actually doing in practice and assembling evidence for my guesses.

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Angelina Wang @angelinawang.bsky.social · 28/10/2025

Cornell (NYC and Ithaca) is recruiting AI postdocs, apply by Nov 20, 2025! If you're interested in working with me on technical approaches to responsible AI (e.g., personalization, fairness), please email me. academicjobsonline.org/ajo/jobs/30971

academicjobsonline.org

Cornell University, Empire AI Fellows Program

Job #AJO30971, Postdoctoral Fellow, Empire AI Fellows Program, Cornell University, New York, New York, US

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Harini Suresh @harinisuresh.bsky.social · 25/04/2025

@michelleding.bsky.social has been doing amazing work laying out the complex landscape of "deepfake porn" and distilling the unique challenges in governing it. We hope this work informs future AI governance efforts to address the severe harms of this content - reach out to us to chat more!

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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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Divya Shanmugam @dmshanmugam.bsky.social · 14/10/2025

I am on the job market this year! My research advances methods for reliable machine learning from real-world data, with a focus on healthcare. Happy to chat if this is of interest to you or your department/team.

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Gabriel Agostini @gsagostini.bsky.social · 03/09/2025

Are you a researcher using computational methods to understand cities? @mfranchi.bsky.social @jennahgosciak.bsky.social and I organize an EAAMO Bridges working group on Urban Data Science and we are looking for new members! Fill the interest form on our page: urban-data-science-eaamo.github.io

urban-data-science-eaamo.github.io

Urban Data Science & Equitable Cities | EAAMO Bridges

EAAMO Bridges Urban Data Science & Equitable Cities working group: biweekly talks, paper studies, and workshops on computational urban data analysis to explore and address inequities.

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Monica Agrawal @monicaagrawal.bsky.social · 15/07/2025

Excited to be at #ICML2025 to present our paper on 'pragmatic misalignment' in (deployed!) RAG systems: narrowly "accurate" responses that can be profoundly misinterpreted by readers. It's especially dangerous for consequential domains like medicine! arxiv.org/pdf/2502.14898

A person searching for risks of surgery. A traditional search engine would surface websites that would likely include both pros and cons of the surgery. However, RAG results only excerpt the cons.

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Serena Booth @reniebird.bsky.social · 14/07/2025

I'll be presenting a position paper about consumer protection and AI in the US at ICML. I have a surprisingly optimistic take: our legal structures are stronger than I anticipated when I went to work on this issue in Congress. Is everything broken rn? Yes. Will it stay broken? That's on us.

A poster for the paper "Position: Strong Consumer Protection is an Inalienable Defense for AI Safety in the United States"

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Allison Koenecke @allisonkoe.bsky.social · 22/06/2025

🎉Excited to present our paper tomorrow at @facct.bsky.social, “Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese”, with @brucelyu17.bsky.social, Jiebo Luo and Jian Kang, revealing 🤖 LLM performance disparities. 📄 Link: arxiv.org/abs/2505.22645

"Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese" Abstract:

While the capabilities of Large Language Models (LLMs) have been studied in both Simplified and Traditional Chinese, it is yet unclear whether LLMs exhibit differential performance when prompted in these two variants of written Chinese. This understanding is critical, as disparities in the quality of LLM responses can perpetuate representational harms by ignoring the different cultural contexts underlying Simplified versus Traditional Chinese, and can exacerbate downstream harms in LLM-facilitated decision-making in domains such as education or hiring. To investigate potential LLM performance disparities, we design two benchmark tasks that reflect real-world scenarios: regional term choice (prompting the LLM to name a described item which is referred to differently in Mainland China and Taiwan), and regional name choice (prompting the LLM to choose who to hire from a list of names in both Simplified and Traditional Chinese). For both tasks, we audit the performance of 11 leading commercial LLM services and open-sourced models -- spanning those primarily trained on English, Simplified Chinese, or Traditional Chinese. Our analyses indicate that biases in LLM responses are dependent on both the task and prompting language: while most LLMs disproportionately favored Simplified Chinese responses in the regional term choice task, they surprisingly favored Traditional Chinese names in the regional name choice task. We find that these disparities may arise from differences in training data representation, written character preferences, and tokenization of Simplified and Traditional Chinese. These findings highlight the need for further analysis of LLM biases; as such, we provide an open-sourced benchmark dataset to foster reproducible evaluations of future LLM behavior across Chinese language variants (this https URL).

Figure showing that three different LLMs (GPT-4o, Qwen-1.5, and Taiwan-LLM) may answer a prompt about pineapples differently when asked in Simplified Chinese vs. Traditional Chinese.

Figure showing that LLMs disproportionately answer questions about regional-specific terms (like the word for "pineapple," which differs in Simplified and Traditional Chinese) correctly when prompted in Simplified Chinese as opposed to Traditional Chinese.

Figure showing that LLMs have high variance of adhering to prompt instructions, favoring Traditional Chinese names over Simplified Chinese names in a benchmark task regarding hiring.

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Shaily @shaily99.bsky.social · 10/06/2025

🖋️ Curious how writing differs across (research) cultures? 🚩 Tired of “cultural” evals that don't consult people? We engaged with interdisciplinary researchers to identify & measure ✨cultural norms✨in scientific writing, and show that❗LLMs flatten them❗ 📜 arxiv.org/abs/2506.00784 [1/11]

An overview of the work “Research Borderlands: Analysing Writing Across Research Cultures” by Shaily Bhatt, Tal August, and Maria Antoniak. The overview describes that We survey and interview interdisciplinary researchers (§3) to develop a framework of writing norms that vary across research cultures (§4) and operationalise them using computational metrics (§5). We then use this evaluation suite for two large-scale quantitative analyses: (a) surfacing variations in writing across 11 communities (§6); (b) evaluating the cultural competence of LLMs when adapting writing from one community to another (§7).

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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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Divya Shanmugam @dmshanmugam.bsky.social · 12/06/2025

I’m in Nashville this week for #CVPR2025! DM me to chat about conformal prediction, test-time adaptation, or model reliability. Excited to see new work and to catch up with friends old and new!!

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Jenn Wortman Vaughan @jennwv.bsky.social · 20/05/2025

Please help us spread the word! 📣 FATE is hiring a pre-doc research assistant! We're looking for candidates who will have completed their bachelor's degree (or equivalent) by summer 2025 and want to advance their research skills before applying to PhD programs.

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Erica Chiang @ericachiang.bsky.social · 01/05/2025

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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Divya Shanmugam @dmshanmugam.bsky.social · 01/05/2025

Erica’s new paper on a method to both measure *and* correct for three types of disparities associated with disease progression is now out! Check out the thread for more detail + findings from a case study on heart failure. Congratulations!!!

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Divya Shanmugam @dmshanmugam.bsky.social · 25/04/2025

my friend jonah made a fun game that i now play everyday: guessten.com! please enjoy and send me your scores

guessten.com

GuessTen

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Divya Shanmugam @dmshanmugam.bsky.social · 09/04/2025

just used this to source citations with great success - a very nice tool!!

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Divya Shanmugam @dmshanmugam.bsky.social · 02/04/2025

kenny had the great idea to spend a whole day analyzing dogs — so so fun! i like health data but turns out i love dog data

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Gabriel Agostini @gsagostini.bsky.social · 28/03/2025

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

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Raj Movva @rajmovva.bsky.social · 18/03/2025

💡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/

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Kenny Peng @kennypeng.bsky.social · 18/03/2025

(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

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Paper Skygest Team @paper-feed.bsky.social · 09/03/2025

Welcome to your personalized Paper Skygest, a curated feed showing posts from your network. 📌 To pin this feed, select the pin icon at the top right of the feed. When you pin it, you'll see it up above here! ➡️ Follow this account for Skygest updates!

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Emma Pierson @emmapierson.bsky.social · 13/01/2025

Our article on using LLMs to promote health equity is out in New England Journal of Medicine AI! 85% of equity-related LLM papers focus on *harms*. But also vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.

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Divya Shanmugam @dmshanmugam.bsky.social · 18/12/2024

We have a new review on generative AI in medicine, to appear in the Annual Review of Biomedical Data Science! We cover over 250 papers in the recent literature to provide an updated overview of use cases and challenges for generative AI in medicine.

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