Papers
2
Total Citations
52
H-Index
2
About
Yangming Zhou is a researcher whose work bridges optimization, artificial intelligence, and robotics, with a particular focus on solving complex combinatorial problems and advancing computer vision. A key contribution is the development of a **Bilevel Memetic Search Approach** for the soft-clustered vehicle routing problem, an extension of the classical capacitated vehicle routing problem where customers in the same cluster must be served by the same vehicle. This work, which has garnered **45 citations**, demonstrates Zhou’s ability to tackle real-world logistics challenges with sophisticated algorithmic design. In parallel, Zhou has made notable strides in autonomous systems through the **OnionNet** framework, an unsupervised method for single-view depth prediction and camera pose estimation from unlabeled video. By introducing LeafNet and ParachuteNet, this work enables robots to infer spatial positions and distances—mimicking human visual perception—without requiring costly labeled data. While newer, this contribution signals Zhou’s versatility in applying machine learning to robotics. Overall, Zhou’s research is characterized by a dual commitment to theoretical rigor in optimization and practical innovation in perception, making their work impactful for both operations research and embodied AI communities.
Research Focus
Key Achievements
Top Papers
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