Research

Our research focuses on the theoretical foundations and practical design of next-generation communication and information systems. In particular, we study fundamental limits and efficient algorithms for wireless networks, information processing, and data-driven systems.

Next-Generation Communications

Future wireless networks are evolving beyond conventional data delivery to become intelligent platforms that integrate communication, sensing, and computing while providing seamless global connectivity. Our research explores the fundamental principles and system designs that enable such next-generation communication systems. In particular, we focus on the following emerging technologies:

Integrated Sensing and Communication [ISAC]
A paradigm that unifies wireless communication and environmental sensing within a single system, enabling networks that can simultaneously transmit information and perceive the surrounding environment.
Reconfigurable Intelligent Surfaces [RIS]
Programmable electromagnetic surfaces that dynamically shape wireless propagation environments, enabling energy-efficient coverage enhancement and improved reliability.
Non-Terrestrial Networks [NTN]
Space–air–ground integrated communication systems that leverage satellites, high-altitude platforms, and aerial nodes to extend connectivity beyond traditional terrestrial infrastructure.
Edge Computing
A distributed computing framework that places computation and intelligence at the network edge, enabling low-latency services and efficient support for data-intensive and AI-driven applications.
Physical-Layer Security [PLS]

Techniques that ensure confidentiality at the physical layer, enabling robust protection against eavesdropping and malicious interference.

Statistical Inference and Machine Learning

In the era of data-driven intelligence, extracting meaningful information from massive and complex data is a fundamental challenge. Our research studies principled methods for inference and learning that enable data to be processed securely, efficiently, and reliably. In particular, we focus on the following topics:

Differential Privacy [DP]
Mathematical frameworks and algorithms that enable statistical analysis and machine learning while rigorously protecting the privacy of individual data.
Generative Models
Learning-based models for data generation and representation, with particular interest in applications such as effective noise design, causal inference, and information hiding .
Federated Learning
Distributed learning techniques that allow multiple devices or organizations to collaboratively train machine learning models without sharing raw data.
Semantic Communications
Learning-based communication paradigms that transmit task-relevant information rather than raw data, enabling more efficient and intelligent data exchange in future networks.

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