Human-Data Interaction Lab.

Our lab is part of the School of Computer and Information Engineering at Kwangwoon University. We explore the visual understanding of complex data, trustworthy AI technologies, and the optimization of Intelligent Transportation Systems (ITS). Our research focuses on analyzing complex data generated across diverse domains to develop interactive systems and intelligent frameworks that can be applied to real-world problem-solving.

We aim to bridge the gap between theoretical data analysis and practical application. By leveraging Kwangwoon University’s engineering heritage, we continue to build systems that enhance human-data interaction and provide reliable solutions for the challenges of modern infrastructure.

  1. [Data Analysis & Visualization] Building Visual Analytics Platforms for Complex Spatiotemporal and High-Dimensional Data

    We design interactive visual analytics environments that enable intuitive exploration of the spatiotemporal context of complex data from various domains, such as urban data, network logs, and moving objects. By leveraging web-based high-performance rendering engines, we build analytical systems capable of visualizing large-scale real-time data without latency, helping users rapidly uncover meaningful patterns hidden within the data.

  2. [AI Reliability & XAI] Human-Centered Explainable AI (XAI) and Model Reliability Verification

    We develop core technologies that visually and transparently explain the reasoning behind black-box AI models, while enabling users to directly interact with data and audit logical flaws or biases in real-time. In high-stakes domains such as healthcare, security, and transportation, we propose interactive analytical frameworks that allow experts to evaluate and improve not only model performance but also validity and explainability.

  3. [Intelligent Transportation System (ITS)] Intelligent Algorithm-Based Traffic Optimization and Decision Support

    We apply advanced metaheuristic and AI optimization techniques, including Reinforcement Learning (RL) and Genetic Algorithms (GA), to solve resource allocation and policy optimization problems involving complex constraints. Through agent-based simulation environments, we explore and learn optimal strategies to develop intelligent decision-support systems that can flexibly respond to the complexity of real-world mobility domains.

Research Summary Research Summary