Signal Processing, Statistical Inference, Reinforcement Learning, and Machine Learning
Statistical signal processing, statistical inference and machine learning are basic tools for making decision or prediction based on incomplete data. This field has been an important branch in signal processing and statistics, and has gained a recent interest in the era of big data and machine intelligence. In this field, SISReL is investigating new possibilities and invention of more efficient inference and learning algorithms based on sparsity, information geometry, statistical methods, and optimization tools. Currently, SISReL is focusing on reinforcement learning from various research perspectives such as value estimation, intrinsic reward design for sparse-reward reinforcement learning, continual learning, parallel learning, multi-agent learning, etc.
Next Generation Communication and Smart Machine Intelligence Systems: 6G, Internet-of-Things
In this area, SISReL is conducting research on wireless communication systems and networks and their fusion with smart machine intelligence systems like connected vehicle from the perspective of real applications such as 5G and beyond, internet-of-things with extensive real world experience of the advisor. We are trying to come up with new algorithms, multi-access methods or system architectures with significant performance improvement for wireless communication networks. Currently, we are investigating proper physical layer technologies suitable for future evolution such as 5G of wirelss access technologies.