Kaidi Yang
Papers
1
Total Citations
6
H-Index
1
About
Kaidi Yang is a rising leader in the intersection of artificial intelligence and intelligent transportation systems, with a primary focus on autonomous mobility-on-demand (AMoD) and reinforcement learning. Her most cited work, "Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems" (2021), introduces a novel framework that leverages graph neural networks to model complex transportation networks, enabling more efficient fleet coordination and dynamic ride-matching for self-driving vehicle fleets. This contribution addresses a critical challenge in urban mobility—how to optimize real-time decision-making in large-scale, networked environments. With 6 citations and growing recognition, Yang’s research bridges deep learning and operations research, offering scalable solutions for next-generation transit. Her work is particularly notable for integrating graph-based representations with reinforcement learning, a methodological innovation that enhances both interpretability and performance. As a researcher, Yang is helping to shape the future of smart cities, where autonomous fleets can reduce congestion and emissions. Her achievements mark her as a key voice in the evolving dialogue on sustainable, AI-driven transportation.
Research Focus
Key Achievements
Top Papers
- 1