Hello! I am an instructor in the Computer Science department at the University of Colorado, Boulder.

I graduated with a Master of Science in Computer Science at Stanford University in 2016. While I was at Stanford, I worked in the Natural Language Processing group and the Literary Lab with Dan Jurafsky and Mark Algee-Hewitt.

Before that, I studied English Literature and Computer Science at the University of Washington, Seattle.

Projects & Presentations

In progress (to be presented February 2020) From Gender to Geography: Avoiding Assumptions by Centering Intentional Language Across Disciplines, with Krishna Pattisapu, Spring Diversity and Inclusion Summit, CU Boulder.

In progress Introduction to Computational Thinking in Python (jupyter notebook based notes for an intro computing course)

Dialogism (data, for information the tool used in the research, contact me directly)

QuoteLi3 (includes data, annotation tool, and attribution code)


All publications prior to 2018 appear under my former legal name.

Grace Muzny, Mark Algee-Hewitt and Dan Jurafsky. 2017. Dialogism in the novel: A computational model of the dialogic nature of narration and quotations. Digital Scholarship in the Humanities. Data bib

Grace Muzny, Michael Fang, Angel X. Chang and Dan Jurafsky. 2017. A Two-stage Sieve Approach to Quote Attribution. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL), Valencia, Spain. Data Tools bib

Grace Muzny, Mark Algee-Hewitt, and Dan Jurafsky. 2016. The Dialogic Turn and the Performance of Gender: the English Canon 1782-2011. In Digital Humanities 2016: Conference Abstracts. Jagiellonian University & Pedagogical University, Kraków, pp. 296-299. bib

Grace Muzny and Luke Zettlemoyer. 2013. Automatic Idiom Identification in Wiktionary. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, USA. Data Code bib

Kate Starbird, Grace Muzny, and Leysia Palen. 2012. Learning from the Crowd: Collaborative Filtering Techniques for Identifying On-the-Ground Twitterers during Mass Disruptions. Accepted to the 2012 Information Systems for Crisis Response and Management (ISCRAM), Vancouver, Canada. bib

More Information

Looking for more information? Look no further, simply send me an email!