Network Based

Network-based research currently focuses on optimizing processes across interconnected systems, aiming to improve efficiency and performance in diverse applications. This involves developing and applying advanced algorithms, including stochastic gradient descent, gradient tracking, and novel neural network architectures like Input Convex LSTMs, to address challenges in distributed optimization, real-time control, and routing. Key areas of investigation include enhancing the speed and scalability of these algorithms, improving their generalization capabilities across different network structures, and mitigating the limitations of machine learning models through network-based optimization techniques. These advancements have significant implications for various fields, from energy systems and manufacturing to social network analysis and online resource allocation.