About

Young Joon Lee

Professor · Cheju Halla University · CIO

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.

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

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

Ongoing Initiatives

RISE (Regional Innovation System & Education)

Leading regional-scale education and innovation programs that connect university capabilities with local industry and public needs.

AI Professor System

Developing an education platform that supports personalized learning through Socratic tutoring, adaptive learning paths, and evidence-grounded assistance. 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.

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.

Courses span NLP, MLOps, Robotics, and Operating Systems, each designed to bridge theory and practice through real projects, open-source tooling, and industry collaboration.

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

Beyond the Lab

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.