Friday, April 4, 2025
10:20 – 11:10 a.m. (CST)
ETB 1020
Zhongyuan Zhao
Research Assistant Professor
Dept of Electrical Engineering and Computer Engineering
Rice University
Title: “Distributed AI in Networked Systems: A Graph-Based Neuro-Symbolic Perspective”
Abstract Artificial intelligence has achieved remarkable success in processing regular, unstructured data, such as language and images, where information is supported on structured grids (tokens in 1D sequences, pixels in 2D grids). However, networked systems—such as communication networks, transportation and logistics systems, and fog/edge/cloud computing—pose fundamentally different challenges for standalone AI. These systems involve parallel and rule-based operations, irregular and dynamic structures, and real-time, high-stakes decision-making, rendering standalone AI systems insufficient. This talk explores graph-based neuro-symbolic approaches that incorporate domain knowledge to enable AI adhere to rules and protocols, learn and adapt with smaller models and less training data, while remaining scalable and interpretable. I will first introduce graph neural networks (GNNs) as a universal workhorse for learning from graph-structured data. Next, we will dive into graph-based deterministic policy gradient (GDPG), a general framework that enhances rather than replaces domain-specific heuristics with GNNs. Using link scheduling in self-organizing wireless networks as an example, we will explore how this approach can tackle key challenges in networked systems—real-time constraints, combinatorial decision spaces, lack of labeled data, and the need for scalability and interpretability. This talk will conclude with an overview of recent advances, potential applications, and broader implications of this framework, including its relevance to biological networks (e.g., protein interaction networks), adaptive traffic signal control, and knowledge graphs.
Biography Dr. Zhongyuan Zhao is a Research Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, working at the intersection of machine learning, network science, wireless communications, and operations research. His research focuses on graph-based neuro-symbolic approaches that integrate the structural, organic, and engineered dimensions of complex, networked systems. By bridging graph-based machine learning and domain-specific analytical models, he has advanced distributed AI architectures, combinatorial optimization, stochastic network optimization, and digital signal processing, leading to scalable and interpretable intelligent solutions for edge computing, routing, scheduling, and baseband signal processing in wireless networks. His work has been published in leading venues in machine learning (ICLR), wireless communications (IEEE TWC, JSAC, TMLCN, etc.), signal processing (IEEE ICASSP, CAMSAP), operations research (M&SOM), and bioinformatics (Brief. Bioinform.). Dr. Zhao collaborates with ARL, USC-ECE, and UNL-Biochem. He has served as Session Chair at ICASSP (2022, 2023) and as a TPC member for IEEE VTC (2020) and Asilomar (2025). He is also a recipient of the Future Faculty Fellowship at Rice University. Dr. Zhao earned his Ph.D. in Computer Engineering from the University of Nebraska-Lincoln, and holds an M.S. in Signal Processing and B.S. in Information Countermeasures Technology from the University of Electronic Science and Technology of China.
Dr. Zhongyuan Zhao’s personal webpage: https://zhongyuanzhao.com/