The Data Science for Social and Behavioral Science Kick Off Event, hosted by the Center for Social & Behavioral Science (CSBS) and the National Center for Supercomputing Applications (NCSA), was designed to bridge the gap between data science and social and behavioral research, and to empower scientists with the tools and knowledge needed to incorporate data science into their work.
Through the examples provided by those already using data science in their work, attendees learned about relevant resources on campus, what specific tools and support are available through NCSA, and about the work of potential collaborators in their field. The event also created a space for attendees to share barriers to adopting data science approaches and identified specific opportunities for collaboration between data scientists and social and behavioral scientists.
William Brady, an Assistant Professor at the Kellogg School of Management, Northwestern University, delivered the keynote for the event. His presentation, “Algorithm-Mediated Social Learning in Online Social Networks,” explored the interplay between human psychology and AI-mediated social contexts. Brady’s research focuses on the dynamics of emotion within social networks and their implications for group behavior, offering insights into how data science can enhance our understanding of digital social interactions.
The panel presentations showcased diverse applications of data science in social and behavioral research. Jessie Chin (School of Information Sciences) discussed the impact of health messaging on public health issues such as HPV and breast cancer, illustrating how data science can inform effective communication strategies. Catharine Fairbairn (Psychology) and Nigel Bosch (School of Information Sciences) presented their use of transdermal biosensors to detect blood alcohol levels in real-time, demonstrating the potential for data science to improve public health monitoring.
Angela Lyons (Agricultural & Consumer Economics) and Aiman Soliman (NCSA) shared their work on geospatial analysis and machine learning to understand poverty and food insecurity among displaced populations, highlighting how data science can address complex social issues. David Molitor (Gies College of Business) examined mortality and well-being in the context of migration due to Hurricane Katrina, as well as workplace wellness programs, showcasing the role of data science in evaluating public policy impacts. Crystal Reinhart (School of Social Work) analyzed Medicaid waiver programs, emphasizing the importance of data science in health policy research.
CSBS Director Eva Pomerantz shared how CSBS is eager to play a role in facilitating data science approaches in social and behavioral science research by connecting researchers to collaborators and resources across campus. NCSA Chief Scientist Daniel S. Katz described some of the data and project management resources available to researchers, including advanced systems and facilities, data storage solutions, integrated database services, networking and security, cutting-edge software and data analysis methods, and comprehensive support for advanced applications and system administration.