Group Testing

Group testing is a powerful technique for efficiently identifying a small number of defective items (e.g., infected individuals, faulty products) within a large population by testing groups of items rather than individually. Current research focuses on improving robustness to errors in test design or execution, developing efficient algorithms leveraging machine learning (like large language models and neural networks) for data analysis and optimization, and exploring adaptive and non-adaptive testing strategies to minimize the number of tests required. These advancements have significant implications for various fields, including disease surveillance, quality control, and high-dimensional data analysis, offering substantial improvements in speed and efficiency compared to individual testing.