Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from “adapting to wireless channels” to “changing wireless channels”.

Bringing sensing and augmentation functions to the intimate surfaces of biological systems underpins technological advances in precision health, biology-machine interactions, and basic scientific discoveries. Contemporary wearable and on-skin sensing devices, produced by centralized mass manufacturing, are usually standardized, rigid, and invariable; they are intrinsically incompatible with the diversity, delicacy, and dynamism of living systems.

The development of efficient and scalable software systems is crucial for the growth of Industry 4.0 and its related applications, such as smart manufacturing and design technologies. In this talk, I will share our projects on developing such systems for automated robot operation, manipulation, and surveillance, with applications in diverse components of Industry 4.0.

Distributed algorithms have been playing an increasingly important role in many applications, such as machine learning, signal processing, and control. Significant research efforts have been devoted to developing and analyzing new algorithms for various applications. In this talk, we provide a fresh perspective to understand, analyze, and design distributed optimization algorithms through the lens of multi-rate feedback control.

Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, the traditional model serving paradigm resorts to the cloud by fully uploading the geo-distributed input data to the remote datacenter.

The development of efficient and scalable software systems is crucial for the growth of Industry 4.0 and its related applications, such as smart manufacturing and design technologies. In this talk, I will share our projects on developing such systems for automated robot operation, manipulation, and surveillance, with applications in diverse components of Industry 4.0. First, I will introduce our research framework, which focuses on the localization and perception of robotic systems.

The development of Autonomous vehicles (AVs) has potential benefits by reducing the number of road fatalities and by improving the accessibility of mobility services. However, these benefits can only be achieved when drivers maintain a calibrated level of trust towards AVs. This report is going to: 1) discuss the definitions and models of human-automation trust; 2) introduce the critical roles of trust in AV acceptance and possible antecedents of trust; 3) discuss trust assessment with multimodal physiological signals.

Small-scale robots have great potential to revolutionize modern medicine by enabling non-invasive medical interventions at hard-to-reach and delicate body locations. However, these robots are inherently limited in actuation, computation, control, and power storage due to space restrictions arising from the miniaturization of conventional robotic components, such as actuators, processors, and sensors. Therefore, it is crucial to develop new original design and fabrication strategies different from those used to build traditional large-scale robots to advance small-scale robots and facilitate their translation from benchtops to bedsides.

Driven by the development of Internet of Vehicles (IoV) and artificial intelligence, enormous smart vehicles are ubiquitously connected to the Internet through cellular networks, with the potential of supporting a plethora of computation-intensive and delay-sensitive transportation applications, such as object classification and autonomous driving. To enhance the computing capability of hardware-constrained vehicles, mobile edge computing (MEC) has recently emerged as a cost-effective paradigm by releasing and distributing computing resources to the edge servers within the radio access networks. In this talk, we focus on pursuing optimal task offloading and resource allocation in MEC by leveraging deep learning and statistical learning techniques.

Road transportation is a complex system with complicated interactions among human, vehicles, and infrastructures. Its emissions, including Greenhouse Gas (GHG) and air pollutants, lead to serious environmental problems that further affect human health and social equity. This seminar will start with a brief introduction on basic terminologies of road transport emissions. With the understanding of the issue, the quantification and characterization methods to analyze vehicle emission and energy consumption will be introduced, and the application scenarios of different model types will be compared. Strategies to mitigate road transport emissions and air quality problems, including vehicle connection & automation, electrification, and travellers’ behavioural guidance, will be proposed and evaluated. As a final remark, policy implications and future research will be discussed.