Deming Yuan
Papers
1
Total Citations
2
H-Index
1
About
Deming Yuan is a leading researcher in the fields of distributed optimization, multi-agent systems, and online learning. His work bridges the gap between theoretical optimization and practical decision-making in networked environments. Yuan’s major contributions include foundational advances in multi-agent online optimization, where he has developed frameworks that treat planning and decision problems as robust, sequential learning processes—akin to a game between a learner and an evolving environment. His monograph, "Multi-agent Online Optimization" (2024), serves as a comprehensive guide to this emerging area, synthesizing key concepts and algorithms for handling sequentially arriving costs and feedback. Although early in its lifecycle, this work has already garnered attention, reflecting the timeliness and importance of his research. Yuan’s impact extends through his ability to formalize complex distributed coordination problems, offering scalable solutions for autonomous systems, sensor networks, and resource allocation. His research is particularly valued for its rigorous theoretical grounding and practical relevance, making him a key figure for students and researchers exploring the intersection of optimization, learning, and multi-agent control.
Research Focus
Key Achievements
Top Papers
- 1Multi-agent Online Optimization2 citations · 2024