Marco Papi
Papers
1
Total Citations
14
H-Index
1
About
Marco Papi is a researcher whose work lies at the intersection of distributed optimization, multi-agent systems, and decision-making under sparse information constraints. His most notable contribution is the development of a suite of distributed methodologies to solve the Sparse Analytic Hierarchy Process (SAHP) problem, a critical challenge in contexts where networked agents—such as wireless sensors, mobile robots, or IoT devices—must be ranked based on utility or importance without centralized coordination. This work, published in 2018 and garnering 14 citations, addresses a fundamental gap in how autonomous systems can collaboratively infer relative priorities using only local, sparse pairwise comparisons. Papi’s approach is particularly impactful for real-world applications where communication bandwidth and energy are limited, offering scalable and robust algorithms that preserve privacy and resilience. His research bridges theoretical rigor with practical deployment, making him a key figure in advancing distributed decision-making for autonomous networks. For students and researchers, Papi’s work exemplifies how classic decision frameworks like AHP can be reimagined for the age of distributed intelligence, offering tools that are both mathematically elegant and operationally viable.
Research Focus
Key Achievements
Top Papers
- 1