Katherine Bennett Ensor, Ph.D., is the Noah G. Harding Professor of Statistics in the George R. Brown School of Engineering at Rice University where she serves as director of the Center for Computational Finance and Economic Systems (CoFES). From 2016 through 2022, she served as the founding director and creator of the Kinder Institute Urban Data Platform, a resource for the greater Houston area. She served as chair of the Department of Statistics from 1999 through 2013. Dr. Ensor, an expert in many areas of modern statistics, develops innovative statistical techniques to answer important questions in science, engineering, and business with a focus on the environment, energy, and finance. She is a fellow of the American Statistical Association, a fellow of the American Association for the Advancement of Science, and has been widely recognized for her leadership, scholarship, and mentoring. She served as President of the American Statistical Association’s (ASA) Board of Directors (2021 – 2022) and as Vice President of ASA’s Board of Directors from 2016-2018. She was a member of the National Academies Committee on Applied and Theoretical Statistics (CATS) from 2015-2021 and currently serves on the Board of Directors of the NSF Institute on Pure and Applied Mathematics (IPAM). She is an Accredited Professional Statistician® (PStat®) and holds a BSE and MS in Mathematics from Arkansas State University and a Ph.D. in Statistics from Texas A&M University.
Abstract: Statistical foundations are without question at the core of modern innovation. In today’s economy, a common phrase is “data is the new gold”. Certainly, we live in an age where data is large, ubiquitous, and comes in many forms. The contributions from the statistical sciences go beyond “data”. We are emerging from a pandemic where statisticians around the globe saved lives by contributing critical understanding to vaccines, treatments, pandemic policies, and management. The contributions are universal – from self-driving cars to Mars rovers, to sustainable and improved infrastructure, to clean energy and environmental stewardship, to financial markets and investing, to advances in medicine and medical practices, and even toward a better understanding of the communities in which we live, work, learn and play. This talk will highlight these important contributions and the innovations they made possible and will look to innovations on the horizon.
Abstract: Of important consideration are multivariate nonlinear dynamic time series with low to high levels of spatial association. We explore a state-space hierarchical modeling approach, considering both a frequentist and Bayesian perspective. Key questions answered are natural clusterings of the time series, short-term deviations between the series, and short-term predictions based on the fitted models. The methodology is applied to fifty weekly time series spanning three years, representing wastewater signals for SARS CoV-2. Wastwater signals are compared to the corresponding observed cases. From this paradigm, a predictive model for emergent diseases is posited.