Joseph M. Phillips

University of North Carolina at Charlotte

Papers

Total Citations

H-Index

About

Joseph M. Phillips is an emerging researcher and educator whose work sits at the intersection of robotics, embedded machine learning, and accessible engineering education. His research focuses on developing practical, low-cost robotic systems that make hands-on learning viable for students and institutions facing financial and technical constraints. In his most-cited work, "Robotic System Control using Embedded Machine Learning and Speech Recognition" (2022, 6 citations), Phillips demonstrated how resource-constrained robotic platforms can be effectively controlled using tiny machine learning and speech recognition — a contribution with direct classroom applicability. Building on this foundation, his 2023 paper on enabling ROS for low-cost educational platforms (2 citations) addresses the complexity of integrating artificial intelligence, localization, and machine vision into affordable systems, broadening student access to sophisticated robotics software. His most recent work (2025) continues this mission by proposing a versatile, budget-friendly robotic system designed to ease the financial burden on both students and academic institutions. While still early in his citation trajectory, Phillips is carving out a meaningful niche in educational robotics, with a clear commitment to democratizing engineering education through innovative, practical, and cost-conscious design.