Diego is practiced at handling large volumes of data to make predictions and business improvements for his clients. He specializes in maintenance planning; fault tree, data, failure mode and effects and criticality analysis; programming in Python; natural language processing; and computer vision. He has authored several publications throughout the course of his career, on topics including risk features using text mining and BERT-based models, and damage assessment of composite sandwich structures.
Having worked on numerous challenging projects throughout his career, Diego strives to develop innovative yet practical solutions for his clients. For example, he worked on a project that used computer vision for image segmentation in the salmon farming industry. He has also worked on several projects focused on optimizing production factories using simulation techniques. On these projects, Diego used queueing theory and discrete event simulation to optimize the production line of a large manufacturer. By employing advanced methods such as reinforcement learning, he developed a custom simulation model that predicted and optimized production line performance, resulting in reduced waste and increased throughput for the client.