Manuel Serna-aguilera is a PhD student and research assistant at the University of Arkansas specializing in computer vision, image processing, and deep learning for image and video analysis. With eight years of hands-on experience across academia and industry, he has applied classical CV techniques and modern deep models—ranging from YOLO-based object tracking to GAN-driven data synthesis—to practical problems like dashcam event classification and automated damage simulation for vehicles. His work bridges reproducible research and applied R&D, leveraging Python/OpenCV, YOLO, and cloud tools from prior internships to move prototypes toward usable datasets and models. Manuel also has teaching experience mentoring introductory programming labs and designing lessons for young learners, reflecting strong communication skills. Based in Rogers, Arkansas, he contributes code and project artifacts publicly on GitHub and collaborates within the CVIU lab to turn research ideas into deployable pipelines. Notably, his GAN internship explored generating labeled synthetic damage to reduce costly data collection and annotation, a pragmatic approach that informs his current PhD projects.
8 years of coding experience
1 year of employment as a software developer
Spanish Language, Spanish Language at Universidad Nebrija
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Arkansas
This repository contains some programs which helped me understand several topics in machine learning (e.g. regression, classification, resampling, trees, SVMs, clustering, neural nets, etc.).
Contributions:30 pushes, 1 branch in 4 years 8 months
Contributions:159 commits, 133 pushes, 1 branch in 3 years 11 months
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Manuel Serna-aguilera - Research Assistant at University of Arkansas