Ramdas Honored for Efforts To Improve Research Reproducibility - Machine Learning CMU - Carnegie Mellon University

Carnegie Mellon University's Aaditya Ramdas, assistant professor in the Department of Statistics & Data Science and Machine Learning Department, has received the National Science Foundation's (NSF) Faculty Early Career Development Award for his project, titled "Online Multiple Hypothesis Testing: A Comprehensive Treatment." "Arguably, one of the major hurdles to reproducibility of scientific studies is the cherry picking of results among the vast array of tests run or quantities estimated," Ramdas said. "We need'online' methods to correct for cherry picking, first acknowledging that the problem exists and then designing algorithms that can account and correct for it." According to Ramdas, statistical methods that improve reproducibility in large-scale scientific studies will combat the increasing public distrust in science. The results of this five-year grant could transform how technological and pharmaceutical industries as well as the sciences perform large-scale hypothesis testing.