High dimensional research involves studying data sets that have a large number of variables or dimensions. This can include data from various fields such as genetics, imaging, social networks, and finance, among others. High dimensional data sets pose challenges in terms of analysis and interpretation due to the complexity and size of the data. Researchers in this area focus on developing statistical methods, algorithms, and models to effectively analyze, visualize, and extract useful information from high dimensional data. Some common techniques used in high dimensional research include dimensionality reduction, feature selection, clustering, and classification methods.