Alexander Glandon

Old Dominion University, Tennessee State University

Papers

Total Citations

H-Index

About

Alexander Glandon is an emerging researcher whose work sits at the intersection of deep learning, computer vision, and human-robot interaction. His research focuses on applying convolutional neural networks and transfer learning techniques to real-world recognition challenges, particularly within robotic systems. In his 2017 paper on CNN transfer learning for face recognition in the NAO humanoid robot — his most cited work with 8 citations — Glandon demonstrated how leveraging pre-trained architectures can offer a practical and computationally efficient alternative to building neural networks from scratch, a contribution with meaningful implications for accessible robotics development. Building on this foundation, his 2020 survey on deep neural networks in speech and vision systems, which has garnered 7 citations, reflects his broader commitment to synthesizing advances across the field and guiding fellow researchers through the rapidly evolving landscape of sensory AI. Though still early in his research career, Glandon's work signals a consistent dedication to bridging theoretical deep learning with applied intelligent systems, making his publications a useful resource for students and practitioners exploring machine perception and autonomous robotics.