Daniel Sewell

Title & Affiliation
Senior Faculty Affiliate Health Policy Research Program
Associate Professor Department of Community & Behavioral Health


  • PhD, Statistics, University of Illinois at Urbana-Champaign
  • MS, Statistics, University of Arkansas, 2010

Daniel Sewell received his PhD in statistics from the University of Illinois in 2015. He is currently an assistant professor of Biostatistics in the College of Public Health at the University of Iowa, a member of the Informatics Initiative, and part of the MInD Healthcare research group. His primary area of research is in statistical models and inference for network data, and in particular the statistical analysis of dynamic social networks. He has also contributed to other subfields of statistics, such as clustering and particle filtering, and holds interest in broad research topic areas such as Bayesian statistics and statistical computation. He has worked collaboratively in the areas of infectious disease, exposure assessment, physical activity accelerometry data, analysis of large health claims databases, and in the area of healthcare team communication. He has been awarded the UI College of Public Health New Faculty Research Award, The UI College of Public Health Junior Faculty Research Opportunity Award, and, along with his collaborators from the University of Illinois, the Patrick J. Fett Award for best paper on the scientific study of Congress and the Presidency. As a graduate student, he was the University of Illinois CGS/ProQuest Distinguished Dissertation Award (limit 1 nomination per university) and was selected as a student presenter at the Midwest Statistical Research Colloquium. He is the Iowa Chapter Representative Of The American Statistical Association (ASA) and is a member of the ASA, Institute Of Mathematical Statistics, the International Network For Social Network Analysis, and the International Society For Bayesian Analysis.

Research Interests

  • Applied Scientific Problems
  • Bayesian Methodology
  • Clustering
  • Machine Learning
  • Monte Carlo Methodology
  • Statistical Computing
  • Statistical Modeling of Network Data