Extracted features from the Media Exposure and Opinion Formation (MEOF) survey. Processed large quantities (millions of rows) of user trace data from YouGov Utilized Python and R for proper data cleaning Segmented and documented code for modularized implementation Optimized Machine Learning models, neural networks, and utilized natural language processing for topic modeling The model performance for ideology is significantly better. GitHub Repository Paper

This project expands on the work by Sewerin, S., Kaack, L.H., Küttel, J. et al. Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset. Sci Data 10, 896 (2023). https://doi.org/10.1038/s41597-023-02801-z. The POLIANNA is a dataset of policy texts from the European Union (EU) that are annotated based on theoretical concepts of policy design, which can be used to develop supervised machine learning approaches for scaling policy analysis.

Artificial Intelligence (AI) has been revolutionary, but its rapid growth has come with significant environmental costs. Training and deploying large-scale AI models require substantial computational resources, resulting in increased energy consumption and greenhouse gas emissions. This tutorial addresses these challenges by introducing energy-efficient deep learning practices and tools to measure, analyze, and mitigate the carbon footprint of AI systems. The goals of this tutorial are: Educate participants on the environmental impact of AI and the importance of sustainable practices.