Susan Murphy, professor of statistics and computer science and Radcliffe Alumnae Professor at the Radcliffe Institute, will receive a Luminary Award at the Precision Medicine 2018 World Conference for her work developing innovative data science methods to improve mobile health care for patients with chronic disease. In recognition of the “highly significant impact” of her work applying statistical methods to improving health care, Murphy has also been chosen to deliver the Fisher Lecture at this year’s Joint Statistical Meetings of the American Statistics Association, which is one of the world’s premier statistics meetings.
Dr. Murphy is a data scientist working on developing data analysis methods and experimental designs to improve real time multi-stage decision-making in mobile health. She focuses particularly on methods and algorithms that can be employed in wearable devices to deliver individually tailored treatments. Murphy also developed the sequential, multiple assignment, randomized trial (SMART). SMART designs provide scientists with the empirical tools to build adaptive interventions, treatment rules that dictate whether, how, and when to alter treatment for patients. SMARTs are currently being used to build better treatments for a broad range of health problems, including cocaine abuse, depression, alcohol abuse, ADHD, autism, and bipolar disorder.
Prior to arriving at Harvard last summer, Murphy had been at the University of Michigan since 1998, most recently as a distinguished University Professor of Statistics, research professor at the Institute for Social Research, and professor of psychiatry at the University of Michigan Medical School. Among her many honors, Murphy was inducted into the National Academy of Medicine in 2014 and into the National Academy of Sciences in 2016 for her distinguished and continuing achievements in original science. In 2013 she was awarded a MacArthur Fellowship for her work developing new methods that evaluate treatment courses for chronic conditions and that allow researchers to test the efficacy of adaptive interventions in clinical trials.
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