Research Approach and Interests

The Signal Processing and AI Group (SIPAI Group) designs numerical optimization algorithms to solve different machine/deep learning problems and apply the solutions to different applications, such as data association for automotive radar, channel estimation and precoder design for wireless communications, data analysis using distributed and federated learning, and graph signal processing and graph learning for communications and biomedical engineering. We leverage our knowledge in a signal processing (SP) and numerical optimization during the design process which allows us to extend conventional model-based SP approaches to model/data-driven or pure data-driven algorithms.

My current research is mainly focused divided into three parts: 1) Graph signal processing (GSP) and graph learning, 2) distributed and federated learning, 3) 6G communications.

· GSP and graph learning

o We have been focusing on online graph learning which tracks on time-varying graph that models non-Euclidean data that lie on an irregular structure. Applications include modeling of cyberattacks, brain, heart, and network traffic, to predict, e.g. the onset of atrial fibrillation (AF).

o We are also investigating the problem of (online) attribute graph clustering using network rolling, graph neural networks, and model agnostic meta learning (MAML), with application to community detection, recommendation systems and fraud detection.

· Distributed and federated learning (DL and FL) in Heterogeneous Networks

o We have been looking at the problem of supervised, self-supervised, and unsupervised distributed and federated learning, with the first two aiming to train MLPs and model-based deep learning models in heterogeneous networks (with statistical and system heterogeneity)

o We are also looking how optimizing communications components can assist in minimizing the impact of asynchronicity in FL

o Distributed online graph learning problem becomes essential to prevent raw data from being transmitted to the cloud for learning task

· Signal processing for 6G communications

o This work has mainly been focused on developing physics-informed neural networks (PINNs) for solving XL-MIMO/HMIMO channel estimation and precoder/beamformer design for 6G systems and intelligent reflective surface-assisted (IRS-assisted) systems by considering nonidealities in the antenna array near-field effects, and polarization. Focus will be on the FR2 (24 – 70 GHz) and FR3 7 – 24 GHz bands

o We also looking at the feasibility of using LLMs and diffusion models to solve optimization models in communications