Data Measurement

Data measurement focuses on quantitatively characterizing the composition and attributes of datasets, analogous to measuring physical objects. Current research emphasizes developing methods for efficient data acquisition and analysis in diverse applications, including machine learning, metrology, and materials science, employing techniques like graph-based semi-supervised learning, variational autoencoders, and Bayesian metamodels. These advancements enable more robust and informed decision-making in various fields by providing quantitative insights into data quality, relevance, and diversity, ultimately improving the reliability and efficiency of data-driven systems.