Divya Shanmugam
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dmshanmugam.bsky.social
Divya Shanmugam
@dmshanmugam.bsky.social
140 followers 200 following 43 posts
On the 2025/2026 job market! Machine learning, healthcare, and robustness
postdoc @ Cornell Tech, phd @ MIT

dmshanmugam.github.io
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Reposted by Divya Shanmugam
jessicahullman.bsky.social
🧠⚙️ 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
November 5, 2025 at 4:40 PM
Reposted by Divya Shanmugam
kpaxdonahue.bsky.social
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!
November 6, 2025 at 6:52 PM
dmshanmugam.bsky.social
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...
The ‘Worst Test in Medicine’ is Driving America’s High C-Section Rate
www.nytimes.com
November 6, 2025 at 6:32 PM
dmshanmugam.bsky.social
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.
paulgp.com Paul Goldsmith-Pinkham @paulgp.com · 9d
Dropping a beta version of this page while everyone is up and processing baseball!

This tool lets you search the full text of papers from the American Economic Review, American Economic Journal series, and over 30,000 NBER working papers.

paulgp.com/econlit-pipe...
Economics Literature Search
Full-text search across 15,000+ papers from top economics journals and NBER working papers. Track how empirical methods have evolved over time.
paulgp.com
November 3, 2025 at 11:48 PM
Reposted by Divya Shanmugam
angelinawang.bsky.social
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
Cornell University, Empire AI Fellows Program
Job #AJO30971, Postdoctoral Fellow, Empire AI Fellows Program, Cornell University, New York, New York, US
academicjobsonline.org
October 28, 2025 at 6:19 PM
Reposted by Divya Shanmugam
harinisuresh.bsky.social
@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!
April 25, 2025 at 6:42 PM
dmshanmugam.bsky.social
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.
October 17, 2025 at 4:29 PM
dmshanmugam.bsky.social
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.
October 14, 2025 at 3:45 PM
Reposted by Divya Shanmugam
gsagostini.bsky.social
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 & 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.
urban-data-science-eaamo.github.io
September 3, 2025 at 3:05 PM
dmshanmugam.bsky.social
can't recommend highly enough!
emmapierson.bsky.social Emma Pierson @emmapierson.bsky.social · Aug 22
🚨 New postdoc position in our lab at Berkeley EECS! 🚨

(please reshare)

We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences!

More info in thread

1/3
August 22, 2025 at 3:42 PM
Reposted by Divya Shanmugam
monicaagrawal.bsky.social
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.
July 15, 2025 at 5:15 PM
Reposted by Divya Shanmugam
reniebird.bsky.social
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"
July 14, 2025 at 1:01 PM
Reposted by Divya Shanmugam
allisonkoe.bsky.social
🎉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.
June 22, 2025 at 9:16 PM
Reposted by Divya Shanmugam
shaily99.bsky.social
🖋️ 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).
June 9, 2025 at 11:30 PM
dmshanmugam.bsky.social
and... here is the actual GIF 🙈
June 14, 2025 at 5:04 PM
dmshanmugam.bsky.social
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.
June 14, 2025 at 3:00 PM
dmshanmugam.bsky.social
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!!
June 12, 2025 at 7:58 PM
Reposted by Divya Shanmugam
jennwv.bsky.social
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.
May 20, 2025 at 2:34 PM
Reposted by Divya Shanmugam
ericachiang.bsky.social
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
May 1, 2025 at 12:57 PM
dmshanmugam.bsky.social
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!!!
ericachiang.bsky.social Erica Chiang @ericachiang.bsky.social · May 1
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
May 1, 2025 at 1:28 PM
dmshanmugam.bsky.social
my friend jonah made a fun game that i now play everyday: guessten.com! please enjoy and send me your scores
GuessTen
guessten.com
April 25, 2025 at 8:59 PM
dmshanmugam.bsky.social
just used this to source citations with great success - a very nice tool!!
ai2.bsky.social Ai2 @ai2.bsky.social · Mar 26
Meet Ai2 Paper Finder, an LLM-powered literature search system.

Searching for relevant work is a multi-step process that requires iteration. Paper Finder mimics this workflow — and helps researchers find more papers than ever 🔍
Screenshot of the Ai2 Paper Finder interface
April 9, 2025 at 12:43 AM
dmshanmugam.bsky.social
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
kennypeng.bsky.social Kenny Peng @kennypeng.bsky.social · Apr 2
Our lab had a #dogathon 🐕 yesterday where we analyzed NYC Open Data on dog licenses. We learned a lot of dog facts, which I’ll share in this thread 🧵

1) Geospatial trends: Cavalier King Charles Spaniels are common in Manhattan; the opposite is true for Yorkshire Terriers.
April 2, 2025 at 2:21 PM
Reposted by Divya Shanmugam
gsagostini.bsky.social
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
March 28, 2025 at 3:25 PM
Reposted by Divya Shanmugam
rajmovva.bsky.social
💡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/
March 18, 2025 at 3:17 PM