Research
Our lab is working both on the study of fundamental theories and on the practical design and optimization of systems in the fields of communication, statistical inference, and machine learning. The followings are some topics on which our lab is currently working on.
Wireless communications
Our research lab specializes in advancing wireless communication technologies, with focus on achieving broader coverage, higher transmission rates, lower latency, and supporting AI-driven services. Our key areas of research include efficient multi-layer communication across terrestrial, aerial, and space layers, optimization of reconfigurable intelligent surfaces (RIS), and edge computing technologies aimed at enabling efficient AI computation.
Secure communications
It's important to make sure that no one listens our conversations without permission. Our research lab is working on how to keep communication safe so that information doesn't leak out. We're also looking into hiding communication, which means keeping the fact that communication is happening a secret. This is especially important in sensitive situations like military communications.
Differential privacy
Our data travels through various paths, like when you do computer searches, play games, or chat with friends on messengers. It's almost always being sent out to the outside world. During this process, unintended privacy leaks can become a serious problem. You might have just rated a movie, but your political preferences or religion could also be exposed. A technology that effectively and prominently protects against these privacy threats is called differential privacy. In our research lab, we're studying differential privacy techniques in a variety of data applications such as big data analysis, machine learning, and metaverse.
Quantum information theory
The 2022 Nobel Prize in Physics was awarded to three individuals for demonstrating quantum entanglement. The intriguing and counterintuitive properties of quantum phenomena like superposition and entanglement are expected to bring about revolutionary advancements in computing and communication. In our research lab, we are focused on studying the fundamental performance improvements that can be achieved by harnessing quantum entanglement for communication and data processing.
Federated learning
Federated learning is a machine learning paradigm where each user's device trains an AI model using their own data, and these individual AI models are then uploaded to a server. The server aggregates the AI models from each user to create a single global model, which is then sent back to the users. Users can further train this global model with their own data and upload it to the server again. By repeating this process, a good AI model, as if it had learned from all users' data, can be developed without the need for user data to leave their devices. Our research lab is working on the development of robust federated learning models against various challenging learning scenarios.