We are a research group in the School of Electronics Engineering at Kyungpook National University. Our lab investigates artificial intelligence (AI) inspired by the computational principles of the human brain. We aim to bridge neuroscience and machine learning to build more adaptive, efficient, and interpretable AI systems capable of reasoning, perception, and interaction.

Our research spans three primary areas:

• Brain-Inspired AI – We study and model neural mechanisms underlying perception and cognition to design algorithms that mimic brain-like processing. This includes work on spiking neural networks (SNNs), bio-inspired learning rules, and neuromorphic computing for robust and energy-efficient AI.

• Generative AI – We develop deep generative models that emulate brain-like creativity, enabling machines to generate, edit, and understand visual and multimodal content. Our research explores diffusion models, neural inversion, and cross-modal learning for data-efficient generative intelligence.

• Physical AI – We design intelligent systems that perceive, reason, and act in the physical world. This involves embodied AI, robot learning, and multi-sensory processing for real-world manipulation and human-AI interaction.

To promote open research and collaboration, we maintain a GitHub organization that shares our code, projects, and lab resources. For more details on our ongoing work, visit our Research page.