Selected Publications

Recent research on the US opioid epidemic has focused on the white or total population and has largely been limited to data after 1999. However, understanding racial differences in long-term trends by opioid type may contribute to improving interventions. We analyzed age-standardized opioid mortality rates, by race and opioid type, for the US resident population from 1979 to 2015.

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

  • National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis

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  • Assessment of Changes in the Geographical Distribution of Opioid-Related Mortality Across the United States by Opioid Type, 1999-2016

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  • Trends in pregnancy-associated mortality involving opioids in the United States, 2007–2016

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University of Toronto

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


Recent Posts

More Posts

Introduction Recently on Twitter, sociologist Phil Cohen put out a survey asking people about their decisions to change their name (or not) after marriage. The response was impressive - there are currently over 5,000 responses. Thanks to Phil, the data from the survey are publicly available and downloadable here for anyone to do their own analysis. However, there’s an issue with using the raw data without lots of caveats: the respondents are not very representative of the broader population, and in particular tend to have a higher education level and are younger than average.


Some great people have compiled historical data on baby names into R packages for both the US (thanks to Hadley Wickham) and Australia (thanks to the Monash group). This makes answering all manner of baby-name-related questions easy. I was interested in looking at the distribution of baby names in these populations over time — that is, how concentrated are name choices in the most popular baby names? Is there a big difference between the number of babies that are called the most popular names compared to other names, or is the distribution more evenly spread?


Professional Experience

University of Massachusetts, Amherst

Graduate Student Researcher, January 2017 – June 2018

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