{"id":1092,"date":"2020-01-23T15:29:20","date_gmt":"2020-01-23T20:29:20","guid":{"rendered":"http:\/\/mathewkiang.com\/?page_id=1092"},"modified":"2024-07-28T17:45:41","modified_gmt":"2024-07-29T00:45:41","slug":"aboutme","status":"publish","type":"page","link":"https:\/\/mathewkiangcom.local\/aboutme\/","title":{"rendered":"About Me"},"content":{"rendered":"

\"GratuitousI<\/span> am an Assistant Professor<\/a> in the Department of Epidemiology and Population Health<\/a> at Stanford University School of Medicine. I received my doctorate\u00a0from the Department of Social and Behavioral Sciences<\/a> at\u00a0Harvard TH Chan School of Public Health<\/a>. Before that, I received my MPH at New York University, my BA in Sociology from San Diego State University, and I was a 2016 fellow at University of Chicago’s Data Science for Social Good<\/a> summer fellowship.<\/p>\n

My research lies at the intersection of computational social science and social epidemiology. Methodologically, my work revolves around combining disparate data sources in epidemiologically meaningful ways. For example, I work with individual-level, non-health data (e.g., GPS, accelerometer<\/a>, and other sensor data from smartphones<\/a>), traditional health data (e.g., survey<\/a>, health systems<\/a>, or death<\/a> certificate<\/a> data<\/a>), and third-party data (e.g., cellphone providers<\/a> or ad-tech data<\/a>). To do this, I use a variety of methods such as joint Bayesian spatial models<\/a>, machine<\/a> learning<\/a>, traditional<\/a> epidemiologic<\/a> models<\/a>, dynamical<\/a> models<\/a>, microsimulation<\/a>, and demographic analysis<\/a>.<\/p>\n

Substantively, my work focuses on socioeconomic and racial\/ethnic inequities in health. A few current projects include (1) my K99\/R00-funded work focused on reducing racial\/ethnic inequities in the treatment of opioid use disorder; (2) NIDA-funded work looking at using social media data to inform the public health response to the opioid overdose crisis; and (3)\u00a0my work with the Stanford Institute on Human-centered Artificial Intelligence<\/a> on how (and if) we can use smartphones to gather data from more generalizable samples.<\/p>\n

Here are quick links to\u00a0my CV<\/a>, my Stanford Profile<\/a>, my Google Scholar profile<\/a>, and my ORCID iD profile<\/a>.<\/p>\n

Collaboration Network<\/span><\/h2>\n

A simple collaboration network below with collaborators in red and collaborations in blue \u2014 hover over a node for more info. (Blog posts for 2022<\/a>,\u00a02020<\/a>, 2019<\/a>, and 2018<\/a>.)<\/p>\n

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