About Me

I have been actively involved as a wireless communication and AI researcher at the Smart Networking Lab (SNL) for about 4 years, starting from my undergraduate studies through my Master’s degree in Computer Science at Chosun University. My expertise lies in the optimization of next-generation wireless networks, focusing on enhancing wireless communication systems through various deep reinforcement learning algorithms. In particular, I designed the multi-objective asynchronous advantage single actor-multiple critics (MO-A3Cs) algorithm for transmission power allocation, proposing a technique that jointly optimizes network spectral and energy efficiency.

For the AI to make decisions and think like a human, multiple sensory information (multimodal) present in the human body is required, and a reinforcement learning algorithm is needed to create and learn a simulated environment and make adaptive decisions. To this end, my greatest interest in R&D is focused on optimizing objective functions and adaptive decision-making based on newly designed reinforcement learning algorithms in a dynamically changing environment. Additionally, my research extends to developing sensor-fusion and multimodal AI models to understand contexts like humans.

As a research assistant, I have acquired and developed expertise in using deep reinforcement learning to optimize objective functions and solve decision-making problems, designing and building wireless testbeds based on software-defined radio (SDR), massive MIMO systems, and IoT/ICT technologies. Recently, I have taken the lead in a project applying multimodal AI techniques for object detection, motion estimation, and tracking using fused WiFi signal and camera data, being responsible for hardware setup and the development of multimodal AI models using the knowledge distillation method.

Lastly, my interests include music, research, and running a YouTube channel where I share the methodologies and results of my research.