COF CIR Chat Session: Data Visualization with Machine Learning: Self-Organizing Maps & Learning Vector Quantization [Miguel Abeldano]

Title: Data Visualization with Machine Learning: Self-Organizing Maps & Learning Vector Quantization 

Miguel Abeldano is a third-year Choose Ohio First (COF) Excellence Fellowship in Computing (EFC) scholar majoring in Computer Science and Engineering with a minor in Environmental Science at The Ohio State University with a passion for artificial intelligence. He is particularly interested in improving machine vision analysis with deep learning models. This past summer, he worked in his hometown of Dayton, Ohio at the Air Force Research Lab on their Autonomy and Capability Team (ACT3). 

His experiences focused on developing advanced Learning Vector Quantization algorithms in C, including Generalized Learning Vector Quantization, Generalized Relevance Learning Vector Quantization, and Dynamic Learning Vector Quantization. Collaborating with a colleague, he integrated these algorithms with Self-Organizing Maps, enabling innovative approaches to multi-dimensional data analysis to improve machine vision. This project challenged him to optimize performance through iterative testing, significantly reducing computational time and improving accuracy, which led to more efficient workflows. The experience sharpened his technical expertise in algorithm development and emphasized the value of collaboration and adaptability in achieving complex goals. By tackling these challenges, he better understood machine learning applications and how they can drive impactful solutions.