S22-041 Reconfiguration of Tabular Data for Discovery of Deep Interaction Features and its Applications in Analysis of Multidimensional Data Stanford scientists have developed a high-performance informatics framework for deep learning analyses of high dimensional (HD) omics data. Md Tauhidul Islam Lei Xing
S19-336 Assumption-Free Contrastive Component Analysis for Finding Data Trends Researchers at Stanford have developed, for the first time, a component analysis algorithm that does not require any assumption on the data structure or data generation process to find out the important components or trends in data. Lei Xing Md Tauhidul Islam