AI and Digital policy • public service • Human-AI-Society interaction
I believe that policy has the power to change people's lives. My vision is to create effective policies that work in practice and contribute to industrial development and social progress.
Currently, as Team Leader of the AI Convergence Team at Ministry of Science and ICT (MSIT), I am advancing policies to diffuse AI across various industries including healthcare and strengthen Korea's AI ecosystem.
I am particularly interested in technical limitations inherent in AI such as hallucinations and bias, exploring both policy and technical approaches to build more trustworthy AI systems.
Led the development of Korea's comprehensive AI strategy to secure 18,000 high-performance GPUs, establish national AI computing infrastructure, and position Korea as a global AI leader. The plan includes the "World Best LLM" project to develop world-class large language models with elite AI teams.
Led comprehensive national strategy to strengthen Korea's AI ecosystem, including securing 18,000 high-performance GPUs, establishing national AI computing centers, and launching the "World Best LLM" project. Coordinated cross-ministerial initiatives for tax incentives, infrastructure development, and global AI talent attraction.
Contributed to the organization of Korea’s landmark AI Summit, bringing together global leaders to establish new frameworks for AI governance and international cooperation.
Organized and coordinated the New York Digital Vision Forum, fostering dialogue on global digital transformation.
Led initiatives to establish AI graduate programs at major Korean universities (KAIST, Seoul National, Yonsei, Hanyang) and cloud-based innovation platforms.
Facilitated Korea's introduction of world-first terrestrial UHD broadcasting and promoted global adoption of ATSC 3.0 standard.
Building technical foundations and research capabilities to pursue doctoral studies at the intersection of AI systems and governance.
Implementing transformer architecture and training pipeline from first principles based on Stanford materials. Building deep understanding of LLM fundamentals—tokenization, attention mechanisms, training dynamics, and scaling laws. This technical foundation is essential for AI policy research grounded in understanding of the systems being governed.
46 policy communications reflecting key initiatives across digital transformation, AI governance, and technology innovation (2016-2025)
AI Convergence Team / Digital Strategy Team
Office of ICT Policy Coordination
Software Policy Bureau
Radio Policy Bureau