Hello! I am Daniel Precioso, a highly experienced and passionate Ph.D. data scientist. My current research revolves around utilizing data science to achieve sustainable development goals and promote ecological transitions. With a degree in Physics, I bring a unique perspective to my work that enables me to develop innovative solutions to complex problems.
Throughout my over 5 years of career on industry, I have gained extensive experience in analyzing and interpreting data, implementing machine learning algorithms, and creating models that help organizations optimize their operations and make informed decisions.
As a data scientist, I am passionate about using my skills and expertise to make a positive impact on society. I believe that data science has the potential to transform the world for the better, and I am dedicated to exploring new ways to leverage this technology for the greater good.
Thank you for taking the time to read my profile 😄 I encourage you to visit my personal website to know about the projects I am enrolled in!
🇪🇺 The Arch 2022, my solution Green Navigation was chosen as the 2nd best to impulse the ecological transition of the European Union. This gave us the chance to pitch at the European Parliament!
👁️ OpenCV AI Competition 2021, 2nd place as member of the team Caleta.
🌊 Ocean Hackathon 2021, 2nd place as member of the project Smart Shipping.
🚢 Hybrid Search method for Zermelo's navigation problem. Under revision.
📖 Applications of Machine Learning and Data Science to the Blue Economy: Sustainable Fishing and Weather Routing. PhD thesis at Universidad de Cádiz, 2023 (Cum Laude).
🐟 Aggregation dynamics of tropical tunas around drifting floating objects based on large-scale echo-sounder data. Marine Ecology Progress Series, 2023 (Q1)
😷 Effectiveness of non-pharmaceutical interventions in nine fields of activity to decrease SARS-CoV-2 transmission (Spain, September 2020-May 2021). Frontiers in Public Health, 2023 (Q1).
🔌 Thresholding Methods in Non-Intrusive Load Monitoring. The Journal of Supercomputing, 2023 (Q2).
🍼 NeoCam: An edge-cloud platform for non-invasive real-time monitoring in neonatal intensive care units. IEEE Journal of Biomedical and Health Informatics, 2023 (Q1).
🐟 TUN-AI: Tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data. Fisheries Research, 2022 (Q2).