“Bridging Mathematical Rigor with Intelligent Systems for Reliable AI”
Foundational AI & Optimization
Trustworthy AI (XAI/TAI)
Robust & Reliable Data Science
AI Inference under Uncertainty and Imprecision
Human-Centric Intelligent Systems and Social Dynamics
Welcome to the Intelligent Systems and Data Science (ISDS) Lab at Sejong University.
Our lab explores intelligent systems through the lens of applied mathematics and data-driven AI, focusing on how learning, inference, and evaluation can be made reliable, eXplainable, inTerpretable, and theoretically sound in real-world environments. We also study human-centric intelligent systems and social dynamics modeling how emotions, interactions, and collective behaviors evolve, and how such dynamics can be leveraged for realistic simulation and decision support.
Research at ISDS Lab develops organically from mathematical principles to algorithmic design and empirical validation. Questions about learning stability, optimization behavior, robustness to imperfect or imprecision data, uncertainty-aware inference, trustworthy evaluation, and human-centered social dynamics are investigated through a continuous cycle of theory, modeling, and experimentation.
We study the theoretical foundations of AI learning algorithms, including loss function design, optimization dynamics, and stability analysis. This area focuses on establishing mathematically grounded principles through new formulations and systematic experimental validation.
We develop methods that enable AI systems to function reliably under noisy, imbalanced, and imperfect data conditions. Our work emphasizes principled robustness and reliability, addressing practical learning challenges encountered in real-world data environments.
We investigate inference problems arising from incomplete, uncertain, or indirectly observed data using soft computing and physics-guided inference frameworks, including inverse and hybrid modeling approaches. This area connects applied mathematics, engineering systems, and AI, serving as a key bridge between theory and application.
We develop mathematical dynamic models of human emotion (e.g., love, happiness, conflict, and addition etc.) and interaction to understand complex and emergent behaviors in intelligent systems operating under chaotic dynamics. This research provides a mathematical foundation for behavior-aware AI, metaverse environments, and digital twin systems.
Contact: jin9135@sejong.ac.kr (Jin Hee Yoon)
#418, Kimwon-gwan, Sejong University, 209, Neungdong-ro, Gwanjin-gu, Seoul, Republic of Korea, 05006
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