Approach and Interests
The AI and
Signal Processing Group (AISP Group) designs numerical optimization algorithms
to solve different machine/deep learning problems and apply the solution 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.
and graph learning
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 and network traffic.
Graph filter, graph neural networks and graph convolutional neural
networks can be used in conjunction with the learned graph to solve community
detection problem related to cyberattacks, prediction of onset of brain
disease, and transceiver design in wireless communication systems.
and federated learning (edge computing) in Heterogeneous Networks
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)
will be looking how to improve learning of non-Euclidean using kernel learning
online graph learning problem becomes essential to prevent raw data from being
transmitted to the cloud for learning task
processing for 6G communications
work has mainly been focused on channel estimation and precoder design in
intelligent reflective surface-assisted (IRS-assisted) systems, using (convex)
optimal design methodology and model-based deep learning, with extension to
using federated learning framework.
plan to investigate IRS-assisted system operating at mid-band frequency of 7 –
24 GHz, which has not been assigned by the 3GPP committee, but is able to
support fast data speed (compared to sub-6 GHz) and reasonable coverage