Task oriented communications, which extracts only task-relevant information for transmission, is envisioned to be a key enabler to alleviate the communication burden in next-generation wireless networks. Thanks to the recent advances in AI, deep neural networks (DNNs) has been introduced for task-relevant information extraction. Nonetheless, most existing work either overly simplifies the wireless channel

Developing effective and computationally efficient statistical models to accommodate nonstationary/nonseparable processes containing both long-range and short-scale variations becomes a challenging task, especially for large-scale datasets with various corruption/missing structures. In this talk, we will introduce Bayesian Complementary Kernelized Learning (BCKL) to achieve scalable probabilistic modeling for multidimensional spatiotemporal data.

Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer-aided design model into a physical object in a layer-by-layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade-off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the

With continuous advancements in cyber transportation technologies, connected and automated vehicle systems (CAVs) are poised to revolutionize the way human drivers navigate roads. Understanding the impact of CAVs on driver cognition and performance is paramount for addressing driver needs and enhancing transportation safety.

In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression.

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.