My research covers a variety of topics in statistics and data science, typically motivated by scientific questions in global health, economics, demography, and sociology. Recent projects include estimating features of social networks (e.g. the degree of clustering or how central an individual is) using data from standard surveys, inferring a likely cause of death (when deaths happen outside of hospitals) using reports from surviving caretakers, and quantifying & communicating uncertainty in predictive models for global health policymakers. More information about OpenVA, our suite of open tools to manage and analyze verbal autopsy surveys, is available here.

I’m the former Editor of the Journal of Computational and Graphical Statistics and a 2019 recipient of the NIH Director’s New Innovator Award. I was elected as a Fellow of the American Statistical Association in 2023.

An description of some of our work recently appeared in the Wall Street Journal and it was used for a visual story in the Washington Post.

I currently an openings for a postdoctoral collaborators. For more information and to apply directly please email me.

A link to my slides from the ASA Webinar on the role of statistics in AI is here.

Technical reports and working papers

• Measurement and modeling of social networks and peer influence
  • Non-robustness of diffusion estimates on networks with measurement error
  • Model-based inference and experimental design for interference using partial network data (R package, code)
  • Asymptotically normal estimation of local latent network curvature (R package, code)
  • General covariance-based conditions for Central Limit Theorems with dependent triangular arrays
  • Data-adaptive exposure thresholds for the Horvitz-Thompson estimator of the average treatment effect in experiments with network interference
  • Spectral goodness-of-fit tests for complete and partial network data (overview and code)
  • Estimating spillovers using imprecisely measured networks (code)
  • Examining Racial Segregation in Associative Networks on Twitter
• Decision-making under uncertainty
  • Robustly estimating heterogeneity in factorial data using Rashomon Partitions (Python package & code)
  • How to measure obesity in public health research? Problems with using BMI for population inference
  • When you come to a fork in the road, take it: the Rashomon effect for social scientists
  • Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models
  • Do we really even need data?
• Global health methodology and data collection strategies
  • Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research
  • Feasible contact tracing
  • Smart containment with active learning: A proposal for a data-responsive and graded approach to COVID-19 (more details)
  • Quantifying the contributions of training data and algorithm logic to the performance of automated cause-assignment algorithms for verbal autopsy
  • Bayesian age category reconciliation for age- and cause-specific under-five mortality estimates
  • Verbal autopsy in civil registration and vital statistics: The Symptom-Cause Information Archive

  Please see arXiv for the most up to date list of working papers.

Affiliations

• University of Washington
  • Professor, Department of Statistics and Department of Sociology.
  • Core faculty member, Center for Statistics and the Social Sciences.
  • Senior Data Science Fellow, eScience Institute.
  • Research affiliate, Center for Studies in Demography and Ecology.
  • Faculty partner, Responsible AI Systems & Experiences (RAISE).