Daniel Cervantes holds a degree in Computer Systems Engineering from the Instituto Tecnológico de Estudios Superiores de Jocotitlán (2019). In 2025, he completed his Master's degree in Engineering Sciences at the Instituto Tecnológico de Toluca, during which he conducted innovative research in deep learning. His primary contribution is the design and implementation of Deep Edition, a methodology that applies sample editing techniques in the latent spaces of deep neural networks to address imbalanced multiclass classification in hyperspectral images. Alongside this work, he participated in other research lines: in collaboration with co-authors, he contributed to confidence-based strategic sample selection for violence detection in images; he also took part in the development of automatic face detection systems using computer vision algorithms embedded in low-cost devices. He is currently pursuing a Ph.D. in Environmental Sciences at the Instituto Tecnológico de Toluca. His research interests focus on computer vision, deep learning, machine learning, and particularly on the challenge of imbalanced class classification in high-dimensionality contexts.