Mr. SaΓΊl Cano-Ortiz, University of Cantabria, Spain
π Mr. Saul Cano-Ortiz is a dynamic academic explorer, seamlessly navigating the intersection of civil engineering and data science. Currently pursuing a Ph.D. in Civil Engineering applied to Data Science at the University of Cantabria Santander, he delves into cutting-edge subjects like Computer Vision and Deep Learning, contributing to his groundbreaking thesis on MAPSIA. With a Master’s in Data Science, where he achieved a stellar CGPA of 9.33/10.00, SaΓΊl showcases expertise in Machine Learning and Data Science Statistics. His journey began with a Bachelor’s in Physics, emphasizing a multidisciplinary approach. As an R&D Data Scientist at the University of Cantabria Santander, he leads diverse projects, including Pavement Distress Detection and road defect recognition using innovative computer vision systems. SaΓΊl’s technical prowess spans Python, TensorFlow, and more. Recognized for his innovation, he triumphed with AI-SIGNTEXT, an award-winning project blending neural networks with real-time sign language transcription. π
πΒ Education and Honors :
π Mr. Saul Cano-Ortiz is a dynamic individual on an academic journey that seamlessly merges civil engineering with the fascinating realm of data science. Currently pursuing a Ph.D. in Civil Engineering applied to Data Science at the University of Cantabria Santander, he immerses himself in cutting-edge coursework including Computer Vision, Deep Learning, and Data Acquisition Systems, all contributing to his thesis on MAPSIA, showcasing his dedication to pushing the boundaries of knowledge.
π€ Building on this foundation, SaΓΊl holds a Master’s in Data Science, majoring in Machine Learning, from the University of Cantabria and Menendez Pelayo International University. His outstanding academic performance, reflected in a CGPA of 9.33/10.00, is complemented by coursework encompassing Machine Learning, Data Science Statistics, Data Mining, Data Life Cycle, and Computer Vision. His master’s thesis, titled “Revealing Invisible,” reflects his prowess in unlocking hidden insights through data science.
π¬ SaΓΊl’s academic journey began with a Bachelor of Science in Physics from the University of Alicante, where he achieved a CGPA of 7.09/10.00. His coursework spanned Applied Physics, Mathematics, and Computational Physics, culminating in a thesis on Interacting Boson Systems. Through his diverse educational background, SaΓΊl embodies a fusion of engineering, data science, and physics, showcasing a multidisciplinary approach to knowledge acquisition. ππ
π Professional Profiles :Β
Scopus
Orcid
Professional Experience :
π¨βπ» SaΓΊl Cano-Ortiz is making waves as an R&D Data Scientist at the University of Cantabria Santander in the Construction Technology Applied Research Group (GITECO) since February 2021. His role is marked by diverse and impactful projects:
π£οΈ MAPSIA: Engaging in Pavement Distress Detection using Deep Learning algorithms from drone images.
ποΈ LIAISON: Spearheading the development of an Improved Computer Vision System based on Generative models for road defect recognition.
π PEMISIA: Involved in InSAR time-series forecasting and prediction of bridge deformation using Machine Learning algorithms.
π XR-Capture: Leading 3D Point Cloud Segmentation for as-built BIM.
π€ Others: Contributing to groundbreaking initiatives, including predicting the ductile-to-brittle transition temperature of a vessel in a nuclear reactor using physics-informed neural networks and employing deep learning for the identification, classification, and tracking of fish using cameras mounted on 3D-printed reefs.
π» SaΓΊl’s technical toolkit includes Python, Scikit-learn, PyTorch, TensorFlow, Keras, OpenCV, Streamlit, NumPy, GPU 3080Ti, MLflow, and Pandas, showcasing his proficiency in cutting-edge technologies. His work exemplifies a fusion of innovation, data science, and technology across a spectrum of applications. ππ§
πAwards :
π SaΓΊl Cano-Ortiz stands as a beacon of innovation, adorned with accolades that reflect his prowess in the realm of technology and entrepreneurship. In 2022, his project, AI-SIGNTEXT, emerged triumphant, winning the Explorer program (Santander X) in Cantabria. This groundbreaking service harnesses the power of neural networks to automatically transcribe Spanish sign language from real-time images into text. Notably, AI-SIGNTEXT was also honored with UCem awards for social responsibility and development projects.
π§οΈ Another feather in SaΓΊl’s cap is AGELESS, an innovation project recognized in 2022 by the E2 program at the Santander International Entrepreneurship Centre. AGELESS introduces a novel conceptβa clothes rack that autonomously covers garments when the probability of rainfall exceeds 85%, leveraging data from the Google weather API.
π The accolades don’t end there. SaΓΊl’s project, “Revealing Invisible,” clinched the Winner title in the 2021 HP Technological Observatory Awards. This initiative involves collaborative efforts between companies and universities, where students undertake master’s theses within a business environment. The culmination of these collaborations results in a prize, and SaΓΊl’s project secured the top spot, earning him an HP Gaming laptop with a GPU 1650. These awards underscore SaΓΊl’s commitment to merging technology, innovation, and practical solutions. ππ
π§ Research Interests π¬π :
π SaΓΊl Cano-Ortiz’s research interests form a dynamic landscape at the intersection of cutting-edge technologies. π His passion lies in the expansive field of Data Science, where he delves into uncovering patterns, insights, and solutions from complex datasets. π€ SaΓΊl’s expertise extends to Computer Vision, harnessing the power of machines to interpret and understand visual information. π‘ His pursuits in Machine Learning and Deep Learning showcase a commitment to developing intelligent systems capable of learning and evolving. π§ Lastly, SaΓΊl explores the realms of Artificial Intelligence, seeking to push the boundaries of what machines can achieve. π His diverse research interests underscore a multidisciplinary approach, poised to shape the future of technology.
Publications ( Top Note ) :
1.Β Pavement Distress Detection: A Deep Learning Approach
Published Year: 2023-02-23
Source: Conference Paper
2.Β Machine Learning Algorithms for Monitoring Pavement Performance
Published Year: 2022-07
Source: Journal Article
3.Β Mosquitonet
Published Year: 2022-11-22
Source: Dataset
4.Β Pavement Distress Detection: A Deep Learning-Based Diffusion Model for Intelligent Road Maintenance
Published Year: 2023-12
Source: Journal Article