Applied AI Systems

Overview

This theme is where our research meet real problem with practical constraints. We build and study applied systems where data is messy, latency matters, and reliability is as important as accuracy — and we feed what we learn in deployment back into the lab’s core research.

The goal is twofold: deliver AI-powered systems that are genuinely useful, and stress-test research ideas against the friction of the real world.

Core Questions

• How do research methods hold up under real-world data, scale, and constraints?
• What makes an ML system dependable, maintainable, and genuinely useful?
• How can AI support teaching, learning, and sequential decision-making under uncertainty?

Representative Work

• Agent-based Instructional Support Chatbot — KCC 2025
• Investigating the Limits of Graph Foundation Model in Real-World Travel Recommendation Systems — PAKDD 2025 GLFM Workshop
• Online Learning with Regularized Knowledge Gradients — PAKDD 2022
• Bias-Corrected Q-Learning With Multistate Extension — IEEE TAC, 2019

See all work on the Publications page.

Related

• Logs tagged Application
• Related logs and events

People

• Donghun Lee — Principal Investigator
• Dayeon Shin — instructional chatbots
• Kyunghee Roh — ML systems and workloads
• Yeajin Lee — applied recognition models

See People for the full lab. Earlier work on recommendation and education systems was led by alumni including Nayoung Lee and Yanggee Kim.