My primary focus is PhD project that assesses situations of health insurance, impact of health seeking delays as well as changes in quality of life, stigma and social support on treatment outcomes. Population is Myanmar migrants who fall ill with tuberculosis in a border area. The project is now in data collection phase.

If you’d like to learn more about what I do, I’d recommend getting started with R and EpiData softwares. Since quantitative research also comes hand in hand with data collection and analysis, my preferred (conventional) data analysis workflow includes EpiData for data collection and R for data manipulation and statistical analysis.

• Statistics with R are serialized webinars that introduce to the fundamentals of descriptive and inferential statistics with an application in R using mStats package. All webinars are made available on YouTube.

• mStats for epidemiological data analysis is intended to make learning R as minimum as possible and get users quickly started with performing data manipulation and statistical procedures in R. It hugely mimics STATA's commands as well as outputs. It is available from CRAN and its development version from github.com.

• Writing a practical guidebook to data collection with EpiData: It is still under development and around two-third is finished.

I also love to explore and learn new techs, like this website which is self-developed and hosted on github.com.