Kim is a DPhil student in Social Data Science and Shirley Scholar at the Oxford Internet Institute. She is interested in the role of technology and globalization on labour market and political outcomes, political economy of inequality, and the future of work. She uses computational social science and causal inference methods to study how institutions, skills, and policy shape labour market outcomes across countries, with interests in skills-based hiring, automation/AI exposure, credentialing, and geographic inequality.
Kim graduated Phi Beta Kappa from Rutgers University-New Brunswick, where she majored in economics and minored in mathematics and English. Kim holds an MPA from Princeton University, where she concentrated in Economics and Public Policy and earned a graduate certificate in Statistics and Machine Learning.
Most recently, Kim worked as a project manager through the 1-year professional program ‘Create Lithuania’, where she worked on projects at the Ministry of Transport and Communication and Lithuania’s Innovation Agency. Previously, she worked at the Federal Reserve Board in Washington, DC where she worked to use data and research to inform the Board about the economic conditions facing low- and moderate-income communities.
Computational Social Science, Future of Work, Political Economy of Inequality, Technological Change and Labor Market Outcomes
