Ryan Carty

Name: Ryan Carty 

Year in program: 8th 

ABD Y/N: Yes

Primary supervisor: Dr. Nwando Achebe; Dr. Jamie Monson 

Major field: African History 

Minor fields: Science, Technology, and the Environment; World History 

Link to your personal page or profile: https://ryattay.me 

A PhD Candidate in the History Department, Ryan Carty explores African history through technology. He approaches technology from a broad perspective, examining objects and techniques in their social, environmental, and material contexts. His dissertation titled “Ruminant Technics: Animal and Material Flows in a West African Transitional Zone, 1948-1985” applies a technical perspective to African livestock history with a geographical focus on Burkina Faso and Ghana. The dissertation chapters examine, for example, the development of new techniques for vaccine production, transportation, and refrigeration. Ryan received research funding from the U.S. Department of Education (Fulbright-Hays DDRA), West African Research Association, MSU College of Social Science, and MSU History Department. Foreign Language and Area Studies (FLAS) Fellowships through MSU’s African Studies Center funded language training in Hausa.

Ryan’s interest in technology extends to the digital humanities. He taught HST 250 History of the Digital Age with a revamped curriculum that took a world historical approach to computing and computing technologies. He worked with Enslaved to develop best practices for the creation of datasets integral to new approaches in the history of slavery. He applies digital methods to his African history research, as well as his work with the Journal of West African History, Matrix: Center for Digital Humanities & Social Sciences, “Visualizing German-Jewish Intellectual Life in the Early Twentieth Century,” and “Sex, Lies, and ‘Tribal’ Politics: Britain’s ‘Treason’ in Her Majesty’s Prized Colony Nigeria.” Ryan develops and designs websites, builds data visualizations and maps, performs text analysis, and extracts, cleans, and analyzes structured datasets.