Data Science
Data science aims to extract meaningful insights from data using computational tools and statistical methods, primarily focusing on prediction, analysis, and knowledge discovery. Current research emphasizes the development and application of large language models (LLMs) and other foundation models for diverse tasks, including automating data analysis pipelines, improving model interpretability, and addressing ethical considerations in AI. This field is crucial for advancing scientific discovery across disciplines and driving innovation in various sectors through data-driven decision-making and the development of intelligent systems.
Papers
AI Competitions and Benchmarks: The life cycle of challenges and benchmarks
Gustavo Stolovitzky, Julio Saez-Rodriguez, Julie Bletz, Jacob Albrecht, Gaia Andreoletti, James C. Costello, Paul Boutros
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning
Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad