Selected Publications

We present a model for estimating neonatal mortality rates for all countries. Neonatal mortality is an important indicator to track progess towards the Sustainable Development Goals. The model is used by the United Nations Inter-agency Group for Child Mortality Estimation.
Demographic Research

We present a Bayesian hierarchical model to estimate age-specific mortality at the subnational level. The model framework overcomes issues with estimating mortality in small populations, is flexible enough to be implemented in a wide variety of situations, and produces estimates of different measures of inequality across regions.

Recent Publications

More Publications

  • Global Estimation of Neonatal Mortality using a Bayesian Hierarchical Splines Regression Model

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  • A Flexible Bayesian Model for Estimating Subnational Mortality

    Details PDF

  • Comparing Temporal Smoothers for use in Demographic Estimation and Projection

    Details PDF Slides Code


University of California, Berkeley

  • Instructor, Formal Demography Workshop, June 2017
  • Instructor, Formal Demography Workshop, August 2015
  • Graduate Student Instructor, Demographic Methods, Fall Semester, 2014

University of Tasmania

  • Tutor, Calculus and Applications I, Semester 1, 2009
  • Tutor, Data Handling and Statistics I, Semester 2, 2008
  • Demonstrator, Chemistry I, Semester 1, 2008


Professional Experience

University of Massachusetts, Amherst

Graduate Student Researcher, January 2017 – Current

World Health Organization

Consultant, September 2016 – June 2017

Data Science for Social Good

Fellow, May 2016 – September 2016

Human Mortality Database

Graduate Student Researcher, January 2015 – May 2016

UNICEF Technical Advisory Group

Consultant, March 2014 – December 2015

The Centre for Aboriginal Economic Policy Research

Research Officer, April 2012 – December 2014

Reserve Bank of Australia

Analyst/Senior Analyst, February 2010 – June 2013

Recent Posts

More Posts

Introduction The Gompertz model is one of the most well-known mortality models. It does remarkably well at explaining mortality rates at adult ages across a wide range of populations with just two parameters. This post briefly reviews the Gompertz model, highlighting the relationship between the two Gompertz parameters, \(\alpha\) and \(\beta\), and the implied mode age at death. I focus on the situation where we only observe death counts by age (rather than mortality rates), so estimation of the Gompertz model requires choosing \(\alpha\) and \(\beta\) to maximize the (log) density of deaths.


A core objective of demographic modeling is finding empirical regularities in age patterns in fertility, mortality and migration. One method to achieve this goal is using Singular Value Decomposition (SVD) to extract characteristic age patterns in demographic indicators over time. This post describes how SVD can be used in demographic research, and in particular, mortality estimation. Background The SVD of matrix \(X\) is \[ X = UDV^T \] The three matrices resulting from the decomposition have special properties: