Javier Ureña Santiago (m), Bachelor’s degree in Industrial Electronic Engineering (BEng, 2018), MsC in Automatics and Robotics (2020) and MsC in Big Data and Visual Analytics (2022)
Passionate about research and development in Computational Science and Artificial Intelligence, I am currently pursuing a doctoral degree at Leopold-Franzens University of Innsbruck. My focus lies in Deep Learning modeling and Artificial Vision systems within the realm of Computational Science. With a diverse background spanning R&D projects at industry-leading companies such as CEMOSA and Expleo Group, I have contributed to the technical advancement of European initiatives under the Horizon Europe Shift2Rail JU. My experience encompasses Python programming for Deep Learning modelling, development of ROUV technologies, optimization energy modelling, and data analysis. My key competencies encompass Computer Vision, Machine Learning, Deep Learning, Programming, Robotics, Automation, Big Data, and Data Science—areas in which I aim to continue innovating and delivering cutting-edge solutions.
Deep learning computer vision data science signal processing
Microorganism enumeration is an essential task in many applications, such as assessing contamination levels or ensuring health standards when evaluating surface cleanliness. However, it's traditionally performed by human-supervised methods that often require manual counting, making it tedious and time-consuming. Previous research suggests automating this task using computer vision and machine learning methods, primarily through instance segmentation or density estimation techniques. This study conducts a comparative analysis of vision transformers (ViTs) for weakly-supervised counting in microorganism enumeration, contrasting them with traditional architectures such as ResNet and investigating ViT-based models such as TransCrowd. We trained different versions of ViTs as the architectural backbone for feature extraction using four microbiology datasets to determine potential new approaches for total microorganism enumeration in images. Results indicate that while ResNets perform better overall, ViTs performance demonstrates competent results across all datasets, opening up promising lines of research in microorganism enumeration. This comparative study contributes to the field of microbial image analysis by presenting innovative approaches to the recurring challenge of microorganism enumeration and by highlighting the capabilities of ViTs in the task of regression counting.
