My research is in the area of applied microeconomics. Through my research, I aim to understand behaviors and resiliency of households and food systems, to expand the boundary of human knowledge and help identify policy solutions. I study consumer behavior, household financial decisions, and attitudes under natural disasters, economic shocks, or policy changes. I also study vulnerability and exposure of food supply chains, and try to understand their resiliency against adverse events. In my research, I pay special attention to socially and economically disadvantaged groups, to understand their interaction with the food supply chain. From a policy perspective, my research aim is to inform government efforts regarding household food insecurity and post-disaster food environments. I use causal inference methods, lab experiments, and machine learning methods, to identify economic parameters of interest.
Since my work requires advanced data methods and data analysis tools, I use methods from computational science, i.e., data science, machine learning, and algorithms. I have worked with a range of datasets: cross-sectional, panel, national surveys, market surveys, and randomized control trials both in the field and lab. I have experience with various data analysis software: Stata, R, Python, Matlab, GAMS, ArcGIS.
I have also worked with organizations in the non-profit and academic sectors. Most of my work involved academic research or project management. I have worked with the World Bank, Harvard Business School, MIT Sloan School of Management, German International Cooperation (in Afghanistan), and CSIS. In my free time, I enjoy cooking, brewing coffee, reading, improving my coding skills, and thinking of solutions for disadvantaged units in a society.