Photo of Dan by Do Pham, Stanford
                    Image: Do Pham, Stanford
Reynolds Professor in Humanities,
Professor of Linguistics
Professor of Computer Science
Stanford University
  I study natural language processing and its application to the social and cognitive sciences. I am a past MacArthur Fellow and also work on the language of food.
Margaret Jacks 117
Stanford CA 94305-2150


TWITTER: @jurafsky



NLP group




From Languages to Information

Tu/Thu 3:00 PM - 4:20 PM, Hewlett 200

CS 324H: The History of NLP   (co-taught with Chris Manning)
Mon 1:30 PM - 4:20 PM, STLC104

CS 329R: Race and Natural Language Processing   (co-taught with Jennifer Eberhardt)
Tue 1:30-4:00 PM

Earlier Courses

CS124:     YouTube lecture videos
2012 NLP Online w/Chris Manning:
    - Youtube channel lecture videos
    - Slides

Speech and Language Processing, Dan Jurafsky and James H. Martin, 3rd edition draft chapters Dan Jurafsky The Language of Food, James Beard Award Finalist


Travis Zack, Eric Lehman, Mirac Suzgun, Jorge A Rodriguez, Leo Anthony Celi, Judy Gichoya, Dan Jurafsky, Peter Szolovits, David W Bates, Raja-Elie E Abdulnour, Atul Butte, Emily Alsentzer. 2024. Assessing the potential of GPT-4 to perpetuat racial and gender biases in health care: a model evaluation study. The Lancet Digital Health, 6:1, e12-e22.

Connor Toups, Rishi Bommasani, Kathleen Creel, Sarah Bana, Dan Jurafsky, Percy Liang. 2023. Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes. NeurIPS 2023.

Peter Henderson*, Eric Mitchell*, Christopher Manning, Dan Jurafsky, Chelsea Finn. Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models. AAAI/ACM Conference on AI, Ethics, and Society, 2023. Honorable Mention for Best Student Paper

Eugenia H. Rho, Maggie Harrington, Yuyang Zhong, Reid Pryzant, Nicholas P. Camp, Dan Jurafsky, and Jennifer L. Eberhardt. 2023. Escalated police stops of Black men are linguistically and psychologically distinct in their earliest moments. PNAS 120 (23).

Anjalie Field, Prateek Verma, Nay San, Jennifer L. Eberhardt, and Dan Jurafsky. 2023. Developing Speech Processing Pipelines for Police Accountability. Proceedings of INTERSPEECH 2023.

Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto. 2023. Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models . EMNLP 2023.

Isabel Papadimitriou and Dan Jurafsky. 2023. Injecting structural hints: Using language models to study inductive biases in language learning. Findings of EMNLP 2023

Tolúlọpẹ́ Ògúnrẹ̀mí, Christopher D. Manning, and Dan Jurafsky. 2023. Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching. Proceedings of Sixth Workshop on Computational Approaches to Linguistic Code-Switching.

Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang. 2023. Foundation Models and Fair Use. Journal of Machine Learning Research, 2023

Mirac Suzgun, Stuart M. Shieber, Dan Jurafsky. 2023. string2string: A Modern Python Library for String-to-String Algorithms. Preprint. arXiv 2023

Myra Cheng, Esin Durmus and Dan Jurafsky. 2023. Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models. ACL 2023. ACL 2023 Social Impact Award.

Martijn Bartelds, Nay San, Bradley McDonnell, Dan Jurafsky and Martijn Wieling. 2023. Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation. ACL 2023.

Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky. 2023. Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding. Findings of ACL 2023.

Dorottya Demszky, Jing Liu, Heather Hill, Dan Jurafsky, Chris Piech. 2023. Can Automated Feedback Improve Teachers’ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial In a Large-Scale Online Course. (Also public draft version here). Educational Evaluation and Policy Analysis, 2023 Nov.

Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, Aylin Caliskan. 2023. Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale. ACM Conference on Fairness, Accountability and Transparency (2023)

Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, James Zou. 2023. When and why Vision-Language Models behave like Bags-of-Words, and what to do about it? International Conference on Learning Representations (ICLR 2023).

Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown and Tatsunori Hashimoto. 2023. When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization. Proceedings of EACL 2023.

Tolúlọpẹ́ Ògúnrẹ̀mí, Dan Jurafsky and Christopher D. Manning. 2023. Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection. Findings of EACL 2023.

Isabel Papadimitriou*, Kezia Lopez*, and Dan Jurafsky. 2023. Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models. Findings of EACL 2023, and SIGTYP 2023

San, N., Bartelds, M., Billings, B., De Falco, E., H., F., Safri, J., Sahrozi, W., Foley, B., McDonnell, B., & Jurafsky, D. (2023). Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions. In Proceedings of the Sixth Workshop on the Use of Computational Methods in the Study of Endangered Languages (ComputEL-6). Association for Computational Linguistics.

Yiwei Luo, Beth Levin and Dan Jurafsky. Taking sides using sentential complement predicates: The interplay of factivity and politeness in persuasion. Proceedings of the 97th Annual Meeting of the Linguistic Society of America. 2023.