Integrating Sociology and Computational Social Science in Research and Careers
Friday, February 27, 2026 11:00 AM to 12:00 PM EST
Online Location
Register here:
https://gmu.zoom.us/webinar/register/WN_0mmX9VMsTFSXV3oFgqsJXw
In this webinar, our panelists will explore how interdisciplinary training can broaden research possibilities and open diverse career pathways. The session highlights two complementary experiences: how a PhD student in Computational Social Science within the College of Engineering integrates sociological theory into his dissertation research, and how a Sociology PhD student incorporates computational and data-driven methods into his dissertation. In other words, how can we build bridges between sociology and computational engineering?
A second objective of the event is to showcase the kinds of careers that emerge from this interdisciplinary training. Our panelists will discuss the roles they currently hold, and how the skills, methods, and conceptual frameworks gained during graduate school have shaped their professional trajectories. We hope to illuminate job pathways for scholars who combine sociology and computational social science.
To guide the discussion, we will explore questions such as:
• Why should a Sociology PhD student take courses in computational social science?
• Why should a Computational Social Science PhD student take courses in sociology?
• How did each benefit from pursuing this cross-training?
• In what ways has interdisciplinary experience influenced their research and work after, or beyond, the PhD?
Our Speakers:
Samuel O. Carter is a complex systems scientist with MITRE and a Ph.D. student at George Mason University in Computational Social Science. He earned his B.S. in Computer Networks and Cybersecurity and his M.S. in Data Analytics from the University of Maryland, and a graduate certificate in Responsible Artificial Intelligence from George Mason University. Sam retired from the U.S. Marine Corps in 2021 after close to 24 years of active service in military intelligence. His role during that time involved modernizing the Marine Corps intelligence community’s use of data engineering and artificial intelligence. Along the way, he completed five combat deployments in support of Operation Iraqi Freedom and Operation Enduring Freedom.
His current research area involves investigating the impact of social media technologies on real-world physical activity. Specifically, he focuses on the sociological implications of technology in everyday life, ranging from the dissemination of information and opinion dynamics to the creation of reality ecosystems across different societal groups. His work delves into how online behavior translates to offline actions, emphasizing sentiment and network analysis to understand the underlying structures that drive behavioral change.
In extremism and radicalization research, he has experimented with systems dynamics methods to uncover the interconnected relationships and causal factors that contribute to changes in conserved populations in response to shifts in the perception of crisis. With algorithmic bias and public policy, he has explored the combination of complexity theory and Keller & Block’s political capitalism to offer an alternative approach to regulatory mediation.
Sean Doody is a data scientist working at the intersection of applied machine learning, data engineering, and social science. His past research combined computational text analysis with qualitative methods and sociological theory to study the rise of contrarian and extreme online communities in our contemporary period of informational surplus and profound technological transformation. His current work focuses on developing machine learning and AI solutions that help identify, quantify, and mitigate online abuse and harassment.
Moderator
John G. Dale, Associate Professor, College of Humanities and Social Sciences, Department of Sociology & Anthropology, Director, Movement Engaged Research Hub, Center for Social Science Research, George Mason University, Fairfax, VA
Organized By:
