In our Machine Intelligence and Information Learning laboratory (MIIL lab), we focus on developing novel machine learning algorithms which overcome challenges for future intelligence systems beyond deep-learning algorithms. The current deep learning algorithms are i) dependent on a massive amount of training data, ii) hard to generalize to unseen tasks and iii) difficult to learn a new concept on the learned knowledge. To tackle these problems, members of our lab are interested in following topics: few-shot learning, meta-learning, lifelong learning (continual and incremental learning), meta-reinforcement Learning and causal learning.
Also, our research interests include intelligence information/communication systems such as 6-Generation wireless communications. For the beyond 5/6G communication systems, resources such as storage/communication and computing capabilities should be deployed on highly-distributed and connected devices or servers for supporting intelligent services (autonomous driving, image recognition, natural language processing, etc.). We are researching about theoretical understanding of distributed learning systems while considering the tradeoffs of communications/storage and computing capabilities. Also, we are interested in developing advanced federated learning algorithms which are suitable for future communication systems.