New version of our preprint! More about the project and data access on our website migrate.tech.cornell.edu

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

*Proud advisor moment* My (first) PhD student Zhi Liu (zhiliu724.github.io) is 1 of 4 finalists for the INFORMS Dantzig Dissertation Award, the premier dissertation award for the OR community. His dissertation spanned work with 2 NYC govt agencies, on measuring and mitigating operational inequities

"Removing the protected bike lane won’t remove cyclists — it will only make the street less safe," the Department of Transportation said in new testimony. "The city risks legal liability for knowingly reducing safety on a Vision Zero priority corridor." buff.ly/QNgRyts

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

Can vision-language models understand figurative meaning in multimodal inputs, like visual metaphors, sarcastic captions or memes? Come find out at our #NAACL2025 poster on Friday at 9am! New task & dataset of images and captions with figurative phenomena like metaphor, idiom, sarcasm, and humor.

I became a dog scientist on April 1st. Now back to normal (a cat scientist).

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

💡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/