Ramdas Honored for Efforts To Improve Research Reproducibility | Machine Learning Department, 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.", inform Stacy Kish, Associate Director, Research Communications at Carnegie Mellon University.

Aaditya Ramdas, assistant professor in the Department of Statistics & Data Science and Machine Learning Department, has received a National Science Foundation Faculty Early Career Development Award.

"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. In addition, it allows Ramdas to fund graduate and postgraduate students to prepare the next generation of researchers...

In this study, Ramdas will address this 'hidden' multiplicity to correct for selection bias that will improve long-term reproducibility. He hopes to develop statistical methods that will protect against the false discoveries using minimal assumptions. Ramdas aims to deliver an open-source software package to enable easier assimilation and application of these methods by other researchers.
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Source: Carnegie Mellon University