The Center for Social Science Scholarship is pleased to continue our workshop series on generative AI, LLMs, and computational social science methods.
This series began with the basics of computing in R and how to use generative AI/LLMs in social science research workflows. Each session will be focused on getting faculty and students comfortable with deploying AI and generative AI models in their research, but with a deeper understanding of the ethical, equity, and environmental consequences of these models.
This series, which is supported through the Elkins Professorship, features several new speakers this spring.
REGISTER
February 20 | 12-1:30pm | PUP 438
Social Network Analysis: Building Web-Based Applications for Experiential Learning
Led by: Dr. Steve McDonald, Professor of Sociology, NC State University
February 27 | 12- 1:30pm | PUP 438
A City in Motion: How Everyday Routines Channel and Control Crime in Baltimore
Led by: Dr. Brian Soller, Associate Professor of Sociology (SAPH), UMBC
April 10 | 12-1:30pm | PUP 438
Geospatial Analysis: Integrating GIS, R, and GeoAI
Led by: Krishna Mummadi, CS3 Graduate Assistant & GES Graduate Student
April 14 | 2-4pm | Walker Avenue, Suite 130 & Webex (hybrid)
Foundations of Large Language Models
Led by: Dr. Josephine Namayanja, Executive Director, iHARP, UMBC
Rhoda Nankabirwa, iHARP Research Assistant and PhD Student, UMBC
April 29 | 12-1:30pm | Webex
Evaluating LLMs for Credible and Rigorous Social Science Research
Led by: Dr. Michael Overton, Associate Professor of Political Science and Public Administration, University of Idaho
May 6 | 12-1:30pm | PUP 438
ML Models for Causal Inference Analysis + HPC Led by: Dr. Eric Stokan, CS3 Director and Associate Professor of Political Science, UMBC
Roy Prouty, Assistant Director for Research Computing, DoIT; UMBC Ph.D. Candidate, Computer Science, CSEE
Sai Vikas Amaraneni, iHARP Research Assistant and UMBC Ph.D. Student
Hosted by the Center for Social Science Scholarship. Cosponsored by the Division of Information Technology, the Center for Scalable Data and Computational Science, and CGC-SCIPE.