Feature Screening

Feature screening aims to efficiently select the most relevant information from large datasets, improving the accuracy and efficiency of subsequent analyses. Current research focuses on applying machine learning, particularly deep neural networks and other algorithms like random forests, to diverse data types including images (e.g., X-rays, facial expressions), text (e.g., social media posts, medical records), and signals (e.g., ECGs), for applications ranging from medical diagnosis to candidate selection. This work is significant because it addresses challenges in high-dimensional data analysis, enabling faster, more accurate, and potentially fairer decision-making across various fields.