Title:

Algorithmic Structures for Emerging Wireless Networks and Statistical Inference in Large Dimensional Spaces



Abstract:

Contemporary challenges in wireless communication and information networks are often framed as problems of inference in large dimensional spaces. Addressing these challenges requires engineering solutions that balance performance optimization with computational efficiency. This summer program aims to lay a mathematical groundwork for innovative approaches in wireless networking. It will cover algorithmic foundations applicable across various engineering fields, such as communications, statistical signal processing, and machine learning. Participants will explore constrained minimum mean-square estimation, delve into multivariate Gaussian distributions, and gain insights into approximate message passing (AMP) algorithms which are known for their efficiency and provable performance metrics. The program will also revisit belief propagation, highlighting its theoretical basis and practical application in decoding specific error-correcting codes. Integrating these concepts, we will develop new communication algorithms tailored to single-user channels, multi-user environments, and massive machine-type communications (mMTC). If time allows, we will extend these methodologies to devise efficient strategies for cell-free systems. The overarching aim is to explore pertinent mathematical tools, apply and evaluate key concepts, and comprehend their influence on engineering practices within wireless networks and related areas.



Keywords:

Information networks, statistical inference, approximate message passing (AMP), belief propagation, graphical models.



Bio:

JF Chamberland is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. He completed a Bachelor of Engineering degree at McGill University and a Master of Science degree at Cornell University. He received a Doctor of Philosophy degree from the University of Illinois at Urbana-Champaign. His research interests are in the areas of communication and information theory, decision and control, computer systems and networks, statistical inference, applied probability, and learning. Recently, he has been studying pragmatic algorithms for wireless networks. His contributions to distributed detection have been recognized through an IEEE Young Author Best Paper Award from the IEEE Signal Processing Society. More recently, he received an IEEE Communications Society & Information Theory Society Joint Paper Award for algorithmic development related to unsourced multiple access. He was the recipient of a Faculty Early Career Development (CAREER) Award from the National Science Foundation. He was invited to present educational innovations at the Frontiers of Engineering Education Symposium, a workshop organized by the National Academy of Engineering. He served as an Associate Editor for the IEEE Transactions on Information Theory, 2017–2020; and he is currently a Senior Area Editor for the IEEE Open Journal of Signal Processing.