Glucose is a universal metabolic substrate, yet its role in regulating protein function—particularly during cell fate transitions—remains poorly understood. During epidermal differentiation, intracellular glucose levels rise significantly. Using azido-glucose click chemistry coupled with dextran-based enrichment and mass spectrometry, we discovered that glucose directly binds to several RNA-binding proteins (RBPs), including the RNA helicases DDX21 and DDX50 and the RNA methyltransferase NSUN2—key regulators of epidermal development. Glucose interacts with the ATP-binding domains of DDX21 and DDX50,...

How can we coordinate self-interested autonomous agents toward desired system outcomes? This question, long central to the field of multi-agent systems, has gained renewed urgency due to the rise of today’s intelligent agents and agentic technologies. We approach this challenge from an optimisation perspective, aiming to find ways to steer agents to optimal correlated equilibria in stochastic environments. I will introduce our recent algorithmic results for this problem. At the core of our approach is...

The number of people working remotely has increased substantially given advancements in video conferencing software.  It has now become easier for individuals to have an immersive online meeting experience, even while traveling. This is due in part to increasingly autonomous features in the car that allow the human driver more opportunities to actively engage in virtual meetings while driving. However, these virtual meetings may also divert the driver’s attention from the road at inopportune times,...

Intelligent systems are vital to modern engineering, enabling autonomy, adaptability, and efficient decision-making. This paper reviews recent advances in learning, estimation, and control—three core pillars of intelligent systems. It covers data-driven and model-based learning methods, including machine learning and reinforcement learning, for decision-making under uncertainty; estimation techniques like distributed and nonparametric methods for dynamic state reconstruction; and robust, optimal, and predictive control strategies for complex, uncertain environments.  The integration of these elements enhances system resilience...

Modeling human driving behavior in highly interactive traffic scenarios is challenging due to the complex game-theoretic interactions and inherent heterogeneity of traffic participants. In this talk, Dr. Chen will first introduce his research on lane-changing (LC) scenarios. A complete LC maneuver consists of both a decision-making stage and an implementation stage. In the decision-making stage, he proposed a Deep Markov Cognitive Hierarchy Model (DMCHM) that integrates both temporal dependencies and game-theoretic processes for lane-changing decisions....

The emergence of connected and automated vehicles (CAVs) is revolutionizing transportation systems by enabling real-time information sharing among traffic participants. The shared data, e.g., photometric images and movement trajectories, offer invaluable opportunities to develop various transportation applications for smart cities. However, most applications rely on deterministic models, ignoring uncertainties in estimations. Given limited data, imperfect models, and inherent dynamics in transportation systems, such point estimators may lead to biased models and suboptimal solutions in system...

Due to the dramatic increase in high data rate services and in order to meet the demands of the sixth-generation (6G) wireless networks, researchers from both academia and industry have been exploring advanced transmission techniques, new network architectures and new frequency spectrum such as the millimeter wave, the terahertz, the infrared, and the visible light spectrum. Light Fidelity (LiFi), in particular, is an emerging technology that has been introduced as a promising solution for 6G...

Climate change and population growth pose unprecedented challenges to the transportation system. Meanwhile, the proliferation of crowdsensing techniques, such as mobile phones, vehicles, social media, and cameras, has generated vast spatiotemporal data for understanding human activities and their interaction with the external environment. Effectively managing such massive, multi-structured spatiotemporal data, extracting valuable travel information, and tailoring solutions to various transportation issues are more crucial than ever. In this talk, I will first delve into my...

The deep analysis of massive mass spectrometry data has long been a critical challenge in the field of proteomics. Traditional mass spectrometry data analysis methods often exhibit low identification rates due to their inability to effectively search for peptides with unexpected digestion patterns and modifications. We developed the pFind 3 method for deep analysis of mass spectrometry data, which expands the search space by five orders of magnitude, enabling effective searches for peptides with any...

The development of artificial general intelligence (AGI) and metaverse technologies hold great potential to enhance the effectiveness of smart cities and societies. we present a cyber-physical-social approach to integrating AGI into metaverse-based smart cities and societies, with a specific focus on Social Intelligence inspired methods. The proposed approach considers the interplay between the physical environment, digital systems, social dynamics, and emerging forms of parallel intelligence. By leveraging these forms of emerged intelligence, the integration of...