Link to PowerPoint presentation: Artificial Intelligence Opportunities for Prevention Science
Artificial Intelligence (AI) and Machine Learning (ML) are transforming scientific fields with new techniques for data analysis, feature discovery, and facilitating research efforts. Recent advancements, particularly in transformer and attention models, along with increased capacity through high-powered computing infrastructures and cloud systems, provide researchers with the resources needed to achieve AI/ML breakthroughs.
In the social sciences, these developments hold significant potential, offering new methods that could lead to entirely new theoretical and practical paradigms. This presentation aims to showcase how transformer and attention models are driving many of these innovations in social science research.
We will explore how AI enhances traditional methods of discovery, measurement, prediction, and causal inference, highlighting current applications, recent accomplishments, future directions, and ongoing projects.
AI-driven models allow researchers to move beyond purely deductive approaches, uncovering unexpected relationships and generating new hypotheses in data-rich environments. Ultimately, we aim to demonstrate how AI can complement and advance existing research methodologies, paving the way for future innovations in the field.
About the Speakers
Jonathan Wright focuses on transforming raw data, often containing sensitive information, into useable extracts for research analysis. He collaborates with a diverse group of stakeholders throughout the transformation process of turning datasets into research products that can be used by researchers, policymakers, and administrators for analysis and planning.
Dr. Wright graduated from the University of Oregon with a Ph.D. in Linguistics, using empirical studies to explore language processing. As a postdoctoral researcher at Penn State, he worked on behavioral and neuroscience studies investigating language processing across the lifespan. His interests involve the processing of qualitative and quantitative research data and the application of natural language processing and machine learning to the investigation of a variety of research questions and needs.
Alex Winters designs, develops, and implements data solutions for researchers and project groups. These systems are instrumental in creating secure, fault-tolerant, and effective solutions which accelerate and improve the project work of analysts, data scientists, and researchers.
Alex holds a master’s degree in Applied Statistics, a dual B.A in Business Administration and Speech Communication, and he is a lifelong learner of science, statistics, and all things data-related. His expertise includes Python, cloud platforms, database management systems, and predictive modeling.
