Baltimore Marriott Waterfront | Baltimore, MD | March 14-17, 2021

Presidential Invited Address

Statistical Leadership: A Pathway to Innovative Interdisciplinary Problem-Solving

F. DuBois Bowman
University of Michigan, School of Public Health

The last year has presented among the most significant challenges in society in over a century. As a nation, we have confronted a public health pandemic of historic proportions, we have experienced increased visibility of racial injustice and calls to upend racism, and we have seen many vital issues play out along divisive lines. The impacts of these experiences have confronted essentially every sector and stressed the need to ground decision-making and discourse in research and science. Many problems facing society are so large, complex, and rapidly evolving that their solutions lie in integrated scientific approaches to problem-solving. Leaders must be equipped with the ability to quickly understand the essence of a problem, determine available data (or possibly collect new data), synthesize and assess information, draw balanced conclusions, and communicate effectively to a range of audiences. Through training, research, and professional practice, statisticians are well-suited to lead across these areas and possess the skills to excel scientifically in any of these domains. It is paramount that we are intentional about training the next generation of statistical leaders and data scientists who not only possess deep analytic expertise but also are poised to lead across disciplinary lines. In this talk, I will draw from my experiences in higher education and recap some key developments over the last year that illustrate why research and scientific thinking matter, the importance of analytic framing (and understanding assumptions), and the value of leaders embracing innovative interdisciplinary problem-solving.

Biography
A renowned expert in the statistical analysis of brain imaging data, F. DuBois Bowman is dean of the University of Michigan School of Public Health. Dr. Bowman’s work mines massive data sets and has important implications for mental and neurological disorders such as Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, and substance addiction. His research has helped reveal brain patterns that reflect disruption from psychiatric diseases, detect biomarkers for neurological diseases, and determine more individualized therapeutic treatments. Dr. Bowman received a Bachelor of Science degree in Mathematics from Morehouse College, a Master of Science degree in Biostatistics from the University of Michigan, and a PhD in Biostatistics from the University of North Carolina at Chapel Hill.