Knowlege and Democracy Lab
Welcome to the Knowledge and Democracy Lab! Based at KAIST, the Lab’s research focuses on applying and developing computational and social scientific methods to better understand how scientific and technological advancements shape politics.
Recruitment
The Lab seeks students who are enthusiastic about integrating theoretical insights with cutting-edge methods to leverage large-scale data for M.S. or Ph.D. degrees in Computational Social Science or Data Science. Students will be recruited through both the School of Digital Humanities and Computational Social Sciences and the Graduate School of Data Science. Please feel free to reach out at taegyoon@kaist.ac.kr for questions about the application process, research opportunities, or to learn more about the Lab’s current projects and ongoing initiatives.
Students
Jihwan Lim: Jihwan is a Ph.D. student in data science. He received his B.A. in Politics from Yonsei University and an M.A. in Science and Technology Policy from KAIST. His research focuses on applying machine learning methods to identify and explain how authoritative allocations of values are made and their impacts on society. He has a particular interest in nuclear non-proliferation and nuclear politics.
Quang Minh Nguyen: Minh is an M.S. student in data science. His current focus is on building natural language processing tools with applications in the reasoning and analysis of socio-political issues on social media platforms. His broader interest is to solve machine reasoning on textual and visual data spaces, which includes—but is not limited to—tasks such as knowledge representation, instruction-following generation, and decision making, potentially with multi-disciplinary applications.
Arnold Hayden: Hayden is an M.S. student in computational social science. With a background in both computer science and political science, his specific research interests concern social media, network mapping and soft power analysis, and predicting societal trends. His methodological focus is on network science and predictive models, utilizing both machine learning and deep learning.