Young Joon Lee

Dr. Young Joon Lee is a Professor of Artificial Intelligence at Cheju Halla University (Jeju, Republic of Korea). His work focuses on building reliable AI systems that can be deployed in education, public-sector problem solving, and data-driven decision-making—especially where language, evidence, and accountability matter.

His recent efforts center on agentic AI (AI agents that plan, retrieve evidence, and execute tasks), retrieval-augmented generation (RAG), and trustworthy multimodal learning. He also studies how large-scale text signals—such as news and institutional communications—shape expectations and behavior in financial economics.

Key Research Interests

• Agentic AI and RAG: multi-agent orchestration, tool use, verification, and human-in-the-loop evaluation
• NLP for Decision-Making: extracting structured signals from unstructured text (media, policy, reports)
• Trustworthy Multimodal Learning: grounding, robustness, and transparency across text–image–data pipelines
• AI for Education: AI tutoring, course generation, learning analytics, and scalable instructional design
• AI for Sustainability and Public Good: climate/energy communication, social acceptance, and evidence-based governance

Leadership and Ongoing Initiatives

• RISE (Regional Innovation System & Education)
  Leading regional-scale education and innovation programs that connect university capabilities with local industry and public needs.

• Agentic AI-Based Intelligent Education Platform (AI Professor System)
  Developing an education platform that supports personalized learning through Socratic tutoring, adaptive learning paths, and evidence-grounded assistance, with a 4-way collaboration (Cheju Halla University × Saltlux × Yonsei University × KAIST)

• AI-Based Autonomous Road Repair Robot
  Leading a national R&D consortium with Seoul National University, KAIST, and RovoRoad to create the world’s first AI-powered autonomous road maintenance system for pothole and sinkhole repair.

• KOICA–TIU–Cheju Halla AI Training Center (Uzbekistan)
  Directing an international capacity-building program to train educators and engineers in responsible AI, GenAI practice, and applied machine learning.

• AI Computing & Research Infrastructure
  Building and operating shared computing resources and MLOps-ready environments to accelerate research, education, and field deployment.

• Sustainable Transition with AI (STAI)
  Contributing to workshops and collaborations that connect AI methods with ESG, climate action, and responsible innovation.

Teaching Philosophy

He designs learning experiences that combine technical rigor with hands-on production. Students learn to build systems end-to-end—data, modeling, evaluation, deployment—and to reason about safety, ethics, and real-world constraints. The goal is to train engineers who can deliver working AI solutions without losing sight of responsibility and human context.

Core Values

• Rigor: measurable claims, reproducible results, and clear evaluation
• Accountability: transparent assumptions, traceable evidence, and responsible deployment
• Collaboration: bridging academia, industry, and public institutions
• Sustainability: aligning AI work with long-term social and environmental needs
• Lifelong learning: staying adaptive as tools, models, and standards evolve

Fun Fact

Outside the lab, he enjoys learning languages (currently Chinese and French) and reading about the philosophy of intelligence—especially what “hallucination” reveals about how models represent knowledge.

Tagline

“Building accountable AI systems for education, sustainability, and real-world decision-making.”