John Cherian is interested in the development of new statistical methods that accelerate scientific research.
A first-year statistics PhD student at Stanford University, he also serves as a statistical consultant to The Washington Post, where he most recently applied and extended a conformal inference method in order to improve the quality of the newspaper’s 2020 presidential election forecast.
Previously, John was a scientific associate at D. E. Shaw Research, where he was involved in the development of improved force fields for biomolecular simulation. While there, his research primarily focused on methods development at the intersection of optimization and statistics.
John received both a bachelor’s degree with distinction in mathematical and computational science and a master’s degree in statistics from Stanford University in 2017. As an undergraduate, he worked with Professor Susan Holmes on the longitudinal analysis of microbiome data.
In addition to research, John enjoys the outdoors, baking overly complicated desserts, and teaching. He grew up in San Jose, California.