Computational Approaches to Humanities Scholarship
The Foundations and Applications of Humanities Analytics (FAHA) workshop is intended for humanities scholars who are in the early stages of applying quantitative and computational methods in their research. While primarily geared towards current PhD students, we welcome applications from those in both earlier and later stages of a research career. The residential, one-week workshop empowers scholars to apply computational tools in their own research and teaching. Participants must complete the FAHA online course prior to the workshop. The in-person workshop will include seminars and tutorials, discussions, and the opportunity to workshop one’s research projects, with the guidance of program faculty. The small, supportive group of fellow participants provide a scholarly community with whom to learn and collaborate on novel humanities analytics projects.
The FAHA institute involves the Santa Fe Institute (SFI), Carnegie Mellon University (CMU), and Princeton University (Princeton).
July 17 – 23, 2022.
The FAHA workshop is a full-time (all-day) commitment. Participants are expected to attend the entire program.
There is no tuition for this program.
Participants are provided with housing and meals at no cost during the program and reasonable travel support to and from Santa Fe, New Mexico.
Program Support The project Foundations and Applications of Cultural Analytics in the Humanities has been made possible in part by a major grant from the National Endowment for the Humanities: Exploring the human endeavor, under Federal Award ID Number HT-272418-20. Any views, findings, conclusions, or recommendations expressed on this page do not necessarily represent those of the National Endowment for the Humanities.
- Gain a theoretical and practical understanding of the computational tools of humanities analytics, their applications, and the interpretation of their outputs.
- Develop skill and confidence in applying these tools to one’s research.
- Build a peer network within which to explore applications of computational analysis to questions in the humanities.
- Diversify the community of humanities analytics scholars, welcoming participants with different perspectives, backgrounds, and interests.
The FAHA program takes place on the campus of the Institute of American Indian Arts (IAIA) in Santa Fe, New Mexico.
Participants are accommodated in student dormitories, a short distance from the FAHA workshop space. The IAIA campus is located in a beautiful Southwest high desert setting with mountain views, extensive walking trails, and quiet places for contemplation, and a short drive to the SFI campus.
David Kinney (Princeton) and Simon DeDeo (SFI, CMU) co-direct the FAHA institute and are the lead instructors. Additional contributing faculty from across the humanities will add to the breadth of perspectives and applications covered in the course.
David Kinney is a postdoctoral research associate at Princeton University and former SFI fellow. He studies epistemology, in particular, the selective acquisition of knowledge. His research applies the principles of probability theory to understand the causal structure of systems in scientific contexts, the formation of group beliefs, and the foundations of scientific reasoning. Within the FAHA project, he will leverage his expertise in probability and information theory, helping students without a mathematics background achieve conceptual mastery.
Simon DeDeo is an external professor at SFI and leads the Laboratory for Social Minds at CMU’s Department of Social and Decision Science. His research group makes use of text sources—from French Revolutionary records and Enlightenment-era scientific communication to online conspiracy theorists and Harry Potter fan fiction—to define factors that influence how and when novel ideas emerge and become accepted. Within the FAHA project, he will provide expertise in the application of information-theoretic and machine learning techniques to case studies in literature and history.
The FAHA institute aims to serve advanced Ph.D. students in the humanities who may have no experience with statistics, computer programming, and/or computer science. Faculty and humanities professionals such as librarians or archivists may also find the program of interest.
The in-person workshop will be limited to 10 participants, selected by application.
Applicants from any country are welcome, though the program focuses on scholars currently studying or employed at institutions in the US, aligned with the program’s support. Accepted applicants who are not US citizens or permanent residents will receive assistance with visa sponsorship as appropriate to their individual circumstances.
- SFI policy requires participants to provide proof of complete and up-to-date COVID-19 vaccination prior to beginning the program. See our FAQs for more information.
- Participants must have completed the online FAHA course prior to the start of the workshop.
- Applicants from the corporate, for-profit sector are not eligible.
The FAHA team is committed to offering inclusive educational programs in which all participants feel valued and supported in their learning journey. We believe that human diversity in all of its dimensions is essential to meaningful scientific progress. We believe that open discourse and respectful sharing of broad perspectives is essential for understanding our world and worlds beyond. We work to ensure our educational programs reflect and encourage this diversity and inclusivity, and we welcome you to join us.
While the application period is open, access the SMApply application system through the "Apply now!" button.
Applicants should submit:
- Biographical information (filled out directly in the application portal)
- Current academic cv or résumé, including list of publications, if any.
- Statement that describes your motivation for incorporating computational text analysis into your scholarship (maximum one page).
- One letter of recommendation from thesis advisor, department/program chair or director, member of thesis committee, or similar.