Flaxman, Abraham

CSDE Research Affiliate

Abraham Flaxman

Associate Professor, Global Health; Associate Professor, Health Metrics Sciences
University of Washington
Tel: 206-897-2802 Box: 358210
website

CSDE Research Areas:

Demographic Measurements and Methods
Health of People and Populations

In the News:

Williams and Flaxman Featured in Article on the Long-Lasting Effects of War (10/24/2024)
CSDE Seminar – Panel: Demographic Methods for Estimating Mortality During Armed Conflict (01/18/2024)
Flaxman and Sharygin Publish On Reconstructing Age Distributions From Census Data with Differential Privacy Controls (10/28/2022)
Comfort, Flaxman, Hagopian, and Colleagues Release New Research in Frontiers in Public Health (01/28/2022)
CSDE Computational Demography Working Group Hosts Abraham Flaxman (11/19/2021)
Flaxman Joins Panel on COVID-19 Experience in Israel (11/15/2021)
CSDE Welcomes Four More Faculty Affiliates! (01/04/2021)
Flaxman Will Contribute Insights during APDU’s Data Privacy, Accuracy, and Access Conference (07/15/2020)
COVID Data, COVID models: Trailing Indicators, Leading Indicators, and the IHME Health Service Utilization Model (05/12/2020)
Spotlight: Beatrix Haddock, Recipient of the Best Poster Award of the CSDE Winter 2020 Trainees’ Lightning Talks (03/21/2020)
Congratulations to Award Recipients for Best Posters during CSDE’s Virtual Lightning Talks and Poster Session! (03/21/2020)

Abraham Flaxman, PhD, is Associate Professor of Health Metrics Science and Global Health at the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. He is currently leading the development of new methods for cost effective analysis with microsimulation and is engaged in methodological and operational research on verbal autopsy. Dr. Flaxman has previously designed software tools such as DisMod-MR that IHME uses to estimate the Global Burden of Disease, and the Bednet Stock-and-Flow Model, which has produced estimates of insecticide-treated net coverage in sub-Saharan Africa. This work uses Integrative Systems Modeling to combine a system dynamics model of process with a statistical model of data to bring together all available sources of information.