SAL > About
About
About the Lab
SAL (Soft Artificial Life) Laboratory is part of the Department of Computer Engineering at Jeju National University. We focus on Intelligent Systems, Artificial Life, Machine Learning, and Optimization Theory. Our goal is to understand the fundamental principles of life through computer simulation and apply them to real-world engineering problems.
Under the guidance of Prof. Seungmin Park, our graduate and undergraduate researchers collaborate on various research projects. We conduct national research projects supported by the National Research Foundation of Korea (NRF) and others, publish our results at domestic and international conferences and journals, and contribute to practical problem-solving through industry collaboration.
Research Areas
Intelligent Systems
Research on the design and application of intelligent systems including multi-agent systems, intelligent control, and brain-computer interfaces (BCI). We aim to implement autonomous decision-making and adaptive systems.
Artificial Life
Studying emergent behavior in multi-agent-based artificial life systems and reproducing essential characteristics of life through computer simulation. We explore core principles of life such as self-organization, adaptation, and self-replication.
Machine Learning
Researching machine learning algorithms such as deep learning and reinforcement learning, and applying them to real-world data-driven problem solving. We focus on model optimization, efficient learning techniques, and real-world applications.
Optimization Theory
Researching nature-inspired optimization algorithms such as evolutionary computation, metaheuristics, and physics-informed neural networks (PINN), and applying them to large-scale combinatorial optimization problems.
Vision
SAL Laboratory aims to contribute to solving complex problems facing humanity through computing technologies inspired by the principles of nature. Our vision is to grow into a world-class research group that explores the essence of life through artificial life simulation and provides innovative solutions to industrial and social problems using machine learning and optimization technologies. Furthermore, we are committed to nurturing the next generation of researchers in a creative and autonomous research environment, contributing to both academia and industry.