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About

Dagang Li is an interdisciplinary researcher whose work spans planetary science, robotics, and intelligent systems, with a particular focus on applying machine learning and deep learning frameworks to complex real-world problems. His most cited contributions demonstrate a remarkable breadth: from detecting Martian dust devils using an improved Faster R-CNN architecture — advancing our understanding of Mars's climate, surface-atmosphere interactions, and aeolian processes — to developing sophisticated neural network frameworks for robot battery management. With over 11 citations each, these works have quickly gained traction in their respective communities since their 2024 publication, signaling meaningful early impact. Li's research also extends into human-robot interaction and assistive technologies. His studies on gait phase recognition for hip exoskeleton systems, employing innovative hybrid models such as CNN combined with HHO-SVM and CNN-LSTM architectures, address a critical challenge in wearable robotics: enabling precise, compliant exoskeleton control to enhance human mobility. By leveraging inertial measurement units and advanced classification models, his contributions push the boundaries of lower-limb rehabilitation engineering. Taken together, Li's portfolio reflects a versatile researcher who bridges space science and assistive robotics through cutting-edge artificial intelligence methodologies.