Ph.D. in Computational Studies of Culture and Society

Program Overview

Our interdisciplinary doctoral program grounds students in the theories and methods of the arts, humanities, and social sciences while training them in complex systems — preparing critical thinkers to engage with large-scale social problems using the tools of computational analysis.

Students are trained in the use of computational and qualitative methods in order to ask critically engaged questions around culture, society, and human behaviors. Our core curriculum provides students with theoretical grounding from a humanities and social scientific perspective, as well as the computational skills needed for analysis of large-scale data sets.

We welcome students with a range of scholarly interests and backgrounds, encompassing such research endeavors as diverse as probing structural inequities embedded within algorithms; proliferation of misinformation through social networks; data-rich approaches to addressing racial inequities in health care; use of AI to detect threats of violence or abuse; data visualization and mapping, and ethnographic analysis of emerging data science cultures.

Balancing a structured core curriculum with elective courses, students gain the content-specific knowledge and area specializations necessary to carry out meaningful research using computational techniques. They also develop the technical expertise for data mining and engaging with large language model methodologies (among other tools), while remaining rooted in intellectual traditions that engage social theory in the interpretation of results. Our students translate their findings across traditional disciplinary boundaries in order for their work to impact the widest possible audience.

Curriculum

Core Classes

• CSCS 7010: Computational Approaches to Humanities & Social Sciences I
• CSCS 7020: Computational Approaches to Humanities & Social Sciences II
• CSCS 7100: Professional Seminar
• CSCS 6110: Ethics for Computational Humanities and Social Sciences
• CSCS 6200: Qualitative Methods
• CSYS 5870: Data Science I
• CSYS 6701: Principles of Complex Systems I

Deadlines

Applications are due on February 15.

Admissions

All students must meet the requirements for Ph.D. admissions as outlined by the Graduate College. Students will also preferably hold a master’s degree in a relevant field and have some prior coursework in computer programming and undergraduate-level applied mathematics.

Pre/Co-Requisites:

• MATH 2522: Applied Linear Algebra
• CS 2240: Data Structures
• STAT 1410: Basic Statistical Methods

Outcomes

Our students gain a rich interdisciplinary grounding, from which they can apply their skills towards impactful, socially engaged career trajectories in academia, industry, non-profit organizations, government, and beyond.

Upon graduation, students will be able to:

1. Contribute original research/scholarship that is relevant and useful to their own leadership practice in their desired professional fields;
2. Apply rigorous computational skills and qualitative methods training in collaborative contexts, in academic contexts as well as in the private, government, health, non-profit and other sectors.
3. Interrogate critical questions of equity and socially engaged problems related to systems of power and privilege;
4. Situate their scholarship, research and computational skills in relationship to theoretical, epistemological, and ontological perspectives;
5. Embrace and critically engage the rapidly emerging context of AI (including large language models and other machine learning technologies).

Career Outlook:

As the ability to quantify various phenomena expands into more diverse domains, the areas in which data scientists are recruited are likewise increasingly varied and diverse. This creates a growing need for skilled professionals in data infrastructure and management, as well as those adept at developing data visualizations and analyses for both academic and public audiences.

Employment opportunities lie in university-based research contexts, particularly within interdisciplinary research centers, as well as in government, journalism, health care systems, and a wide array of corporations. Graduates of the CSCS Ph.D. program are especially attractive to entities looking to leverage the power of big data to identify asymmetrical power relationships, address pressing social problems, and find culturally meaningful explanations from complex systems.

Costs and Funding

Our graduate students are supported by funding from UVM’s College of Arts and Sciences.