PhD Data Scientist with a background in Geoinformatics (MSc) and Computer Science (BSc) keen on solving complex scientific problems through the spatio-temporal analysis of GIS data collections. Currently, I have seven years of specialization in data analysis and machine learning in the fields of climate services and environmental modelling. During this period, citizen science observations have been at the core of my analytical activities, this is why I acquired a broad experience at integrating volunteered observations with heterogeneous geodata sources and modelling them with data-driven methods. In addition to my analytical skills, I am technically versatile, this is why I am comfortable at working with spatial databases, web services, sensor webs or performing GIS analyses. Having good communication skills and a good understanding of computers and algorithms, makes me an effective presenter, capable of conveying complex results to all audiences.
PhD in Geospatial Analytics, 2019
University of Twente
MSc in Geospatial Technologies, 2011
University Jaume I, University of Muenster
BSc in Computer Science Engineering, 2009
University Jaume I
In 2011, the UK Met Office launched a citizen science project intended to collect data from citizen weather stations (CWS) and, to date, the Weather Observations Website (WOW) project has collected over 1,400 million observations worldwide collected by over 17,000 CWS.
Ice formation along the overhead lines is a potential source of infrastructure failure and transportation disruption. Modelling ice formation over structures is challenging because it may occur at a very local scale, often far away from measuring stations.
The European Climate Assessment & Dataset (ECA&D) collects high-quality observational datasets provided by 60+ participant countries. One of the products this organization provides is the E-OBS daily gridded observational dataset, which facilitates the access of the general public to long-term (1950-present) weather gridded layers.
Crosswind is a potential source of road accidents, especially for unloaded trucks. We joined forces with colleagues from the ILT and RWS datalabs to assess how feasible it is to build predictive models capable of identifying risky locations for truck accidents.
Short-duration extreme rainfall events have the potential of causing problems with a high-impact for the road infrastructure and its correct operation. This is why national agencies maintaining the road networks are interested in getting a statistical description for severe downpours, so they can manage the current infrastructure and new constructions keeping the chances of being affected by extreme rainfall low.