Research in the area of embedding focuses on representing data or information in a lower-dimensional space, typically for the purpose of facilitating machine learning algorithms or visualization. Embedding techniques aim to capture complex relationships and patterns in high-dimensional data and map them into a more manageable and interpretable form. Common methods in this area include word embeddings for natural language processing, graph embeddings for network analysis, and image embeddings for computer vision tasks. Embedding research is interdisciplinary, drawing on fields such as statistics, linear algebra, and computer science. The ultimate goal of embedding research is to uncover meaningful insights from large and complex datasets.