I’m a PhD candidate in Linguistics at Stanford University, co-advised by Dan Jurafsky and Rob Podesva. Broadly, my research bridges sociolinguistics, computational linguistics, and psycholinguistics to explore how language encodes social meaning and shapes bias in both humans and AI systems.
In my dissertation, I’m developing machine learning algorithms to detect and model how linguistic variation among Black drivers in Oakland correlates with disparities in police behavior during traffic stops, integrating raciolinguistic theory and psychological experimentation to align model outputs with human perception.
I’ve been supported by the NSF-GRFP as well as Stanford’s DARE, RAISE, and HAI-TEP Fellowships. Outside of Stanford, my research has included collaborations with Google Research, IBM Research, and the Brookings Institution on projects examining LLM neutrality and comparative bias in generative and embedding models, alongside policy-oriented initiatives promoting more inclusive and equitable AI and language technologies.
Research Activity
2025
- Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL.
Jessica Hoffmann, Christiane Ahlheim, Zac Yu, Aria Walfrand, Jarvis Jin, Marie Tano, Ahmad Beirami, Erin van Liemt, Nithum Thain, Hakim Sidahmed, & Lucas Dixon.
EMNLP 2025. [preprint] - Presented dissertation research on instruction-tuning autoregressive models to identify features of African American English (AAE) at the Linguistics for Justice: Advancing Reforms in the Criminal Legal System (July 2025).
- Recipient of the Richard Braddock Award for Best Article of the Year in College Composition and Communication (April 2025).
2024
- Closing the Gap: A Call for More Inclusive Language Technologies.
Chinasa Okolo & Marie Tano.
Research Commentary, Brookings Institution, December 2024. [link] - Moving Toward Truly Responsible AI Development in the Global AI Market.
Chinasa Okolo & Marie Tano.
Research Commentary, Brookings Institution, October 2024. [link]