Samuel Gallego

Mechatronics engineer focused on machine learning, computer vision, and robotics, with experience in backend development and data processing. Skilled in building end-to-end solutions, including model training, data pipelines, and cloud integration. Experienced in applying computer vision to real-world industrial problems for measurement, detection, and quality analysis.

Academic experience at Academic (2024-04 – 2025-12)

• Designed and structure to stabilize an stereoscopic camera (programed on C++, with a gear transmission system for speed and a PID control for speed)
• Designed an algorithm to adquire images of cyclist in order to train a dataset (made on Python)
• Used YOLO-NAS for training the algorithm
• Used OpenCV, Numpy, Matplotlib
• Depurated a dataset for training the algorithm, splited on test and training
• Labeled part of the dataset
• Analized the results of the algorithm and adjusted it in order to work correctly
• Knowledge of ROS2 for node design and simulation, as well as the use of AMCL for the localization of a mobile robot in simulated environments
• K-Means and Fuzzy C-Means used for clustering
• KNN was used for training the algorithm
• Data cleaning was done using Pandas (droped na and 0 values for average values, deleted irrelevant columns, turned categorical variables into numeric labels)

Innovation engineer at Flores el Capiro (2025-10 – Present)

• Trained and implemented a segmentation algorithm in order for measuring cuttings lengths and detect quality errors using YOLOV8 and ROBOFLOW, besides uploading images results to AWS S3 and database to a bucket on AWS
• Trained and implemented a detection algorithm that counted and measured the growth of flower crops using YOLOV8 and ROBOFLOW with an stereoscopic camera, and developed an APP on Flutter to use it
• Welded and installed IOT devices registered on AWS Iot Core in order to detect and control crops' illumination
• Replaced damaged parts of a fumigator robot

Mechatronic engineer in Mechatronics – Institución Universitaria ITM (2020 – 2026)