Daniel Mawunyo Doe
Papers
2
Total Citations
11
H-Index
2
About
Daniel Mawunyo Doe is a pioneering researcher at the intersection of intelligent transportation systems, applied machine learning, and ethical AI. His work primarily focuses on optimizing data management in next-generation vehicular networks and addressing systemic biases in industrial labor markets. In his highly cited 2023 paper, "DSORL," Doe introduced a novel reinforcement learning framework for data source optimization in Vehicular Named Data Networks, directly tackling the immense data challenges posed by High-Dynamic (HD) maps for autonomous driving—a critical step toward enabling real-time, fine-grained environmental awareness. His 2024 work, "Deep Learning and Blockchain-Driven Contract Theory," breaks new ground by proposing a combined deep learning and blockchain architecture to identify and mitigate gender bias in construction recruitment, leveraging teleoperation data to redefine worker qualification criteria. With his most recent publications already garnering significant early citations, Doe is establishing himself as a forward-thinking scholar who bridges technical optimization with social impact, demonstrating that cutting-edge AI can both power autonomous vehicles and promote fairness in the evolving industrial workforce.
Research Focus
Key Achievements
Top Papers
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