Presenter: Santiago Segarra, W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering, Rice University

Description: As communication networks continue to grow in scale and complexity, traditional approaches to network design and operation are becoming inadequate. Machine learning (ML) has garnered significant attention for its potential to complement conventional mathematical models in the capabilities of describing complex wireless systems and deriving computationally efficient solutions. However, standard ML methods, such as multi-layer perceptron (MLPs) and convolutional neural networks (CNNs), struggle to effectively leverage the underlying topology of communication networks, causing significant performance degradation as network size increases. Graph neural networks (GNNs) emerge as a promising ML approach that has gained surging interest within the ML community. GNNs excel when dealing with large network scales and dynamic topologies, outperforming MLPs and CNNs in such scenarios. In this talk, we will provide an accessible introduction to GNNs and highlight their application to critical problems in wireless communications and networking, including power allocation, link scheduling, and computational offloading. The discussion will focus on how GNNs can enhance, rather than replace, existing solutions, achieving superior performance by seamlessly integrating with established methodologies.

Bio: Santiago Segarra received the B.Sc. degree in Industrial Engineering with highest honors (Valedictorian) from the Instituto Tecnológico de Buenos Aires (ITBA), Argentina, in 2011, the M.Sc. in Electrical Engineering from the University of Pennsylvania (Penn), Philadelphia, in 2014 and the Ph.D. degree in Electrical and Systems Engineering from Penn in 2016. From September 2016 to June 2018, he was a postdoctoral research associate with the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He joined Rice University in 2018 as an Assistant Professor and, since July 2024, Dr. Segarra is a W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering at Rice University. He also holds courtesy appointments in the Departments of Computer Science and Statistics. His research interests include network theory, data analysis, machine learning, and graph signal processing. Dr. Segarra received the 2011 Outstanding Graduate Award granted by the National Academy of Engineering of Argentina, the 2017 Penn’s Joseph and Rosaline Wolf Award for Best Doctoral Dissertation in Electrical and Systems Engineering, the 2020 IEEE Signal Processing Society Young Author Best Paper Award, the 2021 Rice’s School of Engineering Research + Teaching Excellence Award, three early career awards (NSF CAREER, ARO ECP, and ARI Early Career), and five best conference paper awards.

Hosted by: Professor Hao Ye, ECE Department