Hi, I’m Raj, a CS PhD student at Cornell Tech. I am advised by Emma Pierson and supported by an NSF graduate fellowship. I’m interested in the social implications of machine learning, with a focus on language models, fairness, and healthcare. Recently, I’ve been studying disagreements and value pluralism in how users interact with LLMs. Before, I’ve published work on how LLM research is becoming increasingly sociotechnical and how coarse race categories undermine algorithmic fairness audits.
Previously, I studied CS at MIT with minors in Women’s & Gender Studies and Biology. I was lucky to work with the Data + Feminism Lab, where we developed intersectional, participatory NLP systems to support activist labor (FAccT 2022). I’ve also interned at Apple Research, studying efficiency and compression for language models [1, 2]. I am very thankful to my excellent mentors along the way, including Harini Suresh, Jonathan Frankle, and Shayne Longpre; see here for a longer list of people who I’m indebted to! I was also president of AI@MIT, where I helped design intro ML workshops and ordered lots of food.
I hew closely to PhD stereotypes: my primary hobbies are baking, rock climbing, reading, mixology, and (mostly watching) tennis. If you’d like to read some unreliable recipes I’ve written, see here.
Updates
- [2024-03] Topics, Authors, and Institutions in Large Language Model Research is accepted as a Main Conference paper at NAACL 2024!
- [2023-12] Our paper on granular race disparities won a Best Paper Honorable Mention at ML4H 2023!
- [2023-11] Data (for 17K LLM papers through Sep 2023) and code for the LLM survey paper are now available on Github! I’ll be presenting a poster at TADA 2023.
- [2023-08] I presented our paper on granular race disparities at MLHC 2023! Thanks for the excellent conversations; code is available on Github.
- [2023-07] New working paper on arXiv! We analyzed 14K language modeling-related papers on arXiv to detail recent publishing trends.
- [2023-04] Our new paper is on arXiv, the first from my PhD! We find that granular race categories are critical to algorithmic fairness analyses in healthcare; here’s a summary.
- [2022-08] Our paper on compressing language models is accepted to COLING 2022! I led this project during an Apple internship in 2021.