I’m a postdoctoral research fellow at the Center for Population Health Sciences at Stanford University School of Medicine. I received my doctorate from Harvard TH Chan School of Public Health in Quantitative Methods from the Department of Social and Behavioral Sciences, and I was a 2016 fellow at University of Chicago’s Data Science for Social Good summer fellowship. A recent version of my CV is here . My public PGP key (along with verified social media accounts) can be found on my Keybase listing .
My dissertation work involved studying how we can use multimodal data (i.e., individual-level high-density mobile phone data, microlevel death certificate data, or population level cell tower data) with spatial modeling, Bayesian methods, and data science to understand health disparities and social and behavioral determinants of health.
My current work is focused on applying these methods to the opioid epidemic, especially racial/ethnic disparities of fatal overdoses. I’m working with Sanjay Basu to more thoroughly incorporate machine learning and causal inference into my research.
- Kiang MV, Basu S, Chen JT, and Alexander MJ. Assessment of changes in the geographical distribution of opioid-related mortality across the United States by opioid type, 1999- 2016. JAMA Network Open (Februrary 2019). doi: 10.1001/jamanetworkopen.2019.0040 [Open Access] [PDF] [Code] [Shiny App] [Press Release] [CNN] [NBC]
- Gemmill A, Kiang MV, and Alexander MJ. Trends in pregnancy-associated mortality involving opioids in the United States, 2007-2016. American Journal of Obstetrics and Gynecology (January 2019). doi: 10.1016/j.ajog.2018.09.028 [Code] [Press Release]
- Vable AM, Kiang MV, Glymour MM, Rigdon J, Drabo E, & Basu S, Performance of matching methods to un-matched ordinary least squares regression under constant effects‡. SER Annual Meeting 2018: Baltimore, MD (June 2018). ‡Best student poster award.
- Kiang MV, Alexander MJ, Zhang Z, & Chen JT, The geographical distribution of opioid mortality by race in the United States, 1999–2016: Identifying epidemic hotspots. PAA Annual Meeting 2018: Denver, CO (April 2018) [Interactive Supplement] [Code] [Slides]
- Kiang MV, Staples P, Torous J, Alexander MJ, & Onnela JP, A new platform for high density data collection in population health research.PAA Annual Meeting 2017: Denver, CO (April 2018) [More info]
- Course developer (x2), Using Python for Research via HarvardX V1 (Nov 2016) and V2 (Jan 2018), HSPH, Professor Jukka-Pekka Onnela
- Instructor, Intro to Causal Inference for Data Science Workshop (March 2017), Instituto Tecnológico Autónomo de México
- Teaching assistant (x2), Dynamics of Infectious Disease (Spring 2017 and Spring 2018), HSPH, Professor Caroline Buckee
- Teaching assistant, Society and Health (Fall 2013), HSPH, Professor Ichiro Kawachi
- Teaching assistant, Biostatistics II (Spring 2010), NYU, Professor Ying Lu
- Teaching assistant, Statistics I (Fall 2009), NYU, Professor Robert Norman
Current Working Papers
- Kiang MV, Krieger N, Buckee CO, Onnela JP, & Chen JT. Decomposing the geographic distribution of black-white inequalities in premature mortality in the United States, 2010–2015. [Under Review]
- Vable AM, Kiang MV, Glymour MM, Rigdon J, & Basu S. Inferences from matching methods for statistical regression. [In Press — February 2019]
- Kiang MV, Chen JT, Buckee CO, Krieger N, & Onnela JP. The human factors of digital phenotyping: Evaluating the feasibility of mobile phones in health inequalities research.
- Kiang MV, Onnela JP, Krieger N, Chen JT, & Buckee CO. Predicting dengue in Thailand: Comparing administrative and empirical areal units.
†Authors contributed equally.
‡Best student poster award.