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.

Created a “Debt Settlement App” for Data Structures and Algorithms Course. The app is able to do the following: Add users to the application Create groups and add new members to the group Add expenses in the group and let the application handle all the splitting When confused about the overall expenses, go and check the Personal and Overall Dashboard Further more this task involved: Back-end Front-end Algorithm selection Working with databases GitHub Repository Note: The Live Demo is currently unavailable Presentation Slides