Welcome

Kathy Ensor
Katherine Bennett Ensor, Ph.D., PStat®,
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.

Known globally for her methods in statistics and data science, Dr. Ensor’s research has applications in quantitative finance and risk management, energy, and public and environmental health. Her numerous initiatives have bridged business, science and engineering and have served society.

Dr. Ensor has a strong history of service to the statistics, applied sciences and engineering communities. She is a member of the ABET-CSAB Board of Directors and provides accreditation criteria in statistics and data science. She serves on the Board of Scientific Counselors for the National Institute of Environmental Health Sciences (NIEHS) and the Board of Trustees for the NSF Institute for Pure and Applied Mathematics (IPAM). She was President of the American Statistical Association’s (ASA) Board of Directors (2021 – 2022) and Vice President of ASA’s Board of Directors from 2016-2018 and was a member of the National Academies Committee on Applied and Theoretical Statistics (CATS) from 2015-2021.

Dr. Ensor is a fellow of ASA and AAAS and has been recognized for her leadership, scholarship, and mentoring and was inducted in 2021 to the Texas A&M College of Science Academy of Distinguished Former Students. Ensor 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.

Research: Dr. Ensor develops methods for dependent data including:

  • Time series / Spatial and Spatial-Temporal / High-dimensional
  • Unique Applications of Bayesian Hierarchical Modeling and Approximate Bayesian Computation
  • Stochastic Process Modeling and Information Integration

Primary Application Areas Include: