David Bamman

David Bamman is an Associate Professor in the School of Information at UC Berkeley, specializing in applying Natural Language Processing (NLP) and machine learning to cultural and social science questions. He leads research in born-literary NLP, computational humanities, and cultural analytics, with affiliated roles in EECS, Linguistics, and Computational Precision Health. Bamman holds degrees from Carnegie Mellon (Ph.D., 2015), Boston University (M.A., 2006), and University of Wisconsin-Madison (B.A., 1998). His work is supported by NEH, NSF, and industry grants.

• Educations:
  • Ph.D. in Computer Science (2015), Carnegie Mellon University
  • M.A. in Applied Linguistics (2006), Boston University
  • B.A. in Classics (1998), University of Wisconsin-Madison
• Research Interests: NLP for underserved domains (e.g., literature, social media), coreference resolution, cultural analytics, and computational methods for studying literature and culture. Projects include LitBank and BookNLP datasets.
• Grants & Awards: Hellman Fellow (2019), Amazon Research Award (2017), NSF CAREER Award, and NEH funding.
• Teaching: Courses include Natural Language Processing (Info 159/259), Computational Humanities (INFO 190), and Applied NLP (INFO 256).

His research group explores topics like racial representation in high school literature, Hollywood diversity metrics, and the sociocultural implications of LLMs. Bamman advises multiple PhD students and collaborates on datasets like CMU Book Summaries and 11K Latin Books.