My scientific work combines theoretical research in statistical methodology with applied research that involves complex problems with massive data. The methodological work is on selective and simultaneous inference (multiple comparisons), and centers on the “False Discovery Rate” (FDR) criterion, as well as on general methods for data analysis, data mining and data visualization. My current interest is the replicability problem in science: too often, the results of studies gaining headlines cannot be replicated by other experimenters. Part of the problem is the use of statistical tools that fail to address the challenge of selective inference. I am trying to develop statistical tools that will aid researchers to cope with this problem, from the areas of Medicine, Epidemiology, Genomics, Bioinformatics, Neuroscience and behavior.