Network optimization leverages graph theory to enhance traditional optimization methods, modeling real-world systems as networks of nodes and links. Nodes can represent diverse entities (e.g., stores, vehicles, or computers), while links represent interactions such as deliveries or data transfers, often weighted by frequency or volume. Algorithms like shortest path, minimum-cost flow, and maximum flow help solve practical challenges in transportation, supply chains, and logistics. Classic problems such as the traveling salesman and vehicle routing extend these ideas to complex real-world scenarios involving scheduling, capacity, inventory, and routing constraints. Overall, network optimization offers powerful tools for uncovering patterns and enhancing efficiency in systems where connectivity and flow are crucial.

This series is hosted by the NC State Data Science and AI Academy as part of Data Science and AI Week. The series brings leading experts from academia, industry and government to campus for in-depth talks on the latest challenges and innovations in data science and artificial intelligence. Each session highlights real-world applications, research breakthroughs, and professional pathways, giving students, faculty and partners the opportunity to learn directly from distinguished practitioners.

This series is designed to spark conversation across disciplines, showcase the impact of data science and AI in diverse fields, and connect the NC State community with thought leaders shaping the future of technology.