Yesheng Chen

China National Petroleum Corporation (China), Grinnell College

Papers

Total Citations

H-Index

About

Yesheng Chen’s research bridges artificial intelligence and education, with a focus on computer vision and computational thinking. In their most cited work, “DeepSORT with siamese convolution autoencoder embedded for honey peach young fruit multiple object tracking” (2024, 16 citations), Chen developed a novel tracking framework that integrates a siamese convolutional autoencoder with DeepSORT, significantly improving the accuracy of detecting and tracking young fruit in agricultural settings—a contribution with practical implications for precision farming. Earlier, Chen explored computing education in “A Middle-School Code Camp Emphasizing Digital Humanities” (2019, 14 citations), designing and evaluating a camp that introduced middle-school students to programming through digital humanities projects, addressing national calls for early computer science exposure. This work demonstrated how interdisciplinary approaches can make computing accessible and engaging for younger learners. Chen’s research thus spans two impactful domains: advancing deep learning techniques for agricultural monitoring and developing innovative educational interventions to broaden participation in computing. Their work has garnered citations from both technical and educational research communities, reflecting its cross-disciplinary relevance and potential for real-world application.