Dan Jurafsky

About

Dan Jurafsky is Professor of Linguistics, Professor of Computer Science, and Reynolds Professor in Humanities at Stanford University. He is an award-winning teacher and a MacArthur Fellow, recognized for his significant contributions to the fields of linguistics and computer science. Jurafsky has received the Richard C. Atkinson Prize in Psychological and Cognitive Sciences from the National Academy of Sciences and is a member of both the National Academy of Sciences and the American Academy of Arts and Sciences. Additionally, he is a Fellow of the Association for Computational Linguistics, the American Association for the Advancement of Science, and the Linguistics Society of America. His research and teaching focus on language models and other natural language processing tools, emphasizing their applications to the cognitive and social sciences as well as to social good. Jurafsky is the author of the widely-used online textbook "Speech and Language Processing" and the 2015 international bestseller and James Beard Award-nominee, "The Language of Food." He earned his Ph.D. in Computer Science in 1992 from the University of California at Berkeley.

Research topics

• Computer Science
• Artificial Intelligence
• Political Science
• Natural Language Processing
• Sociology
• Machine Learning
• Linguistics
• Mathematics
• Law
• History

Selected publications

• On the Opportunities and Risks of Foundation Models

  arXiv (Cornell University) · 2021 · 2169 citations

  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(…

  PublisherOA PDFDOI

• Racial disparities in automated speech recognition

  Proceedings of the National Academy of Sciences · 2020 · 656 citations

  Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems…

  PublisherOA PDFDOI

• Towards the Systematic Reporting of the Energy and Carbon Footprints of\n Machine Learning

  arXiv (Cornell University) · 2020 · 306 citations

  Accurate reporting of energy and carbon usage is essential for understanding\nthe potential climate impacts of machine learning research. We introduce a\nframework that makes this easier by providing a simple interface for tracking\nrealtime energy consumption and carbon emissions, as well as generating\nstandardized online appendices. Utilizing this framework, we create a\nleaderboard for energy efficient reinforcement learning algorithms to\nincentivize responsible research in this area as an…

  DOI

• Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

  Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2021 · 125 citations

  Senior authorCorresponding

  Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, Dan Jurafsky. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

  PublisherOA PDFDOI

• Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks

  AERA Open · 2020 · 116 citations

  Senior authorCorresponding

  Cutting-edge data science techniques can shed new light on fundamental questions in educational research. We apply techniques from natural language processing (lexicons, word embeddings, topic models) to 15 U.S. history textbooks widely used in Texas between 2015 and 2017, studying their depiction of historically marginalized groups. We find that Latinx people are rarely discussed, and the most common famous figures are nearly all White men. Lexicon-based approaches show that Black people are de…

  DOI

Recent grants

• RI: Small: New tools for studying structural and inductive bias in NLP models

  NSF · $500k · 2021–2024

• RI: Medium: Deep Understanding: Integrating Neural and Symbolic Models of Meaning

  NSF · $1.1M · 2015–2019

Frequent coauthors

• Mirac Süzgün

  University Hospital of Bern

  30 shared

• Wido van Peursen

  25 shared

• Sophia L. Pitcher

  University of Cambridge

  25 shared

• Jacobus A. Naudé

  25 shared

• Elizabeth Robar

  Vrije Universiteit Amsterdam

  25 shared

• William A. Ross

  25 shared

• William Jones

  25 shared

• Randall Buth

  25 shared

Labs

• Stanford Natural Language Processing GroupPI

  Performing groundbreaking Natural Language Processing research since 1999.

Awards & honors

• MacArthur Fellow

Similar researchers at Stanford University

• Christopher D. Manningh-149
• Beth Levinh-39
• Meghan Sumnerh-10