PhD Dissertation Proposal Defense: Janice Yu Zhen Chen, Toward Resource-Efficient Decision-Making for Networked Systems
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Speaker
Abstract
Networked systems are prevalent in critical domains such as environmental monitoring, healthcare, wireless communication, and online recommendation systems. As these systems continue to scale, optimizing resource efficiency—across sample collection, energy usage, communication, and computational resources—has become a critical challenge. This dissertation focuses on developing resource-efficient decision-making policies to address key challenges in various networked systems.
The first part of this dissertation addresses parameter estimation in wireless sensor networks, where sensing, data collection, and transmission must be balanced under per-time-slot energy limitations to maximize estimation accuracy. The second part focuses on quickest change detection, aiming to minimize sample complexity while accounting for uncertainty in change timing and distributions. The third part extends to multi-agent systems, designing communication-efficient policies that enable cooperative bandit learning in applications like multi-server recommendations and distributed clinical trials. Finally, motivated by networked decision problems naturally modeled as graph problems, we propose to explore whether quantum computing can provide computational advantages in solving hard graph problems, such as detecting planted bipartite clique.
This dissertation advances resource-efficient learning, estimation, and decision-making, contributing both theoretical insights and practical algorithms for scalable, reliable networked systems.
Advisor
Don Towsley