Design and build a solution for modeling cyber risk in industrial infrastructures. The aim is to develop a system that, based on evidence obtained from different data sources, can make dynamic measurements or estimates of the probability and impact of the different cyber incidents that could occur. Once these measures or estimates allow modeling the cyber risk, the visualization of the results obtained will be complete, updated, and understandable, so that it will be possible to support decision-making.


Develop a comprehensive platform to analyze, visualize and evaluate sustainable tourism indicators. This system will allow to explore relevant data on sustainable tourism, using advanced visualization and analysis tools. It will include an interactive component that will facilitate informed decision making by generating predictions and recommendations based on machine learning models.


Application of Artificial Intelligence in the study of predictive factors of evolution in hereditary transthyretin Amyloidosis.


Collaboration in the development of the SIAGRO tool with the TEST & TRIALS company. SIAGRO is a statistical software with a user-friendly interface to analyse data, carry out statistical process control, exploratory data analysis, pattern discovery, predictive modelling, with the ultimate goal of aiding decision making and instant quality control.


Collaboration with the company Deimos Space S.L. and the Madrid City Council in the improvement of the classification models of the Madrid Decide database.


Collaboration with the company Spika Tech. S.L. for holographic and non-invasive visualisation with the aim of measuring all the electrical signals produced by the heart to prevent and diagnose heart health.


The aim of this study is to design and validate a prediction model of ambulation capacity one month after suffering a hip fracture. It is a multicentre, analytical and observational study. The method consists of using data from patients included in the National Hip Fracture Registry (RNFC) during the period between 2017 and 2020 and designing a predictive model of clinically significant deterioration of gait (loss of autonomy or total loss of the ability to ambulate) one month after the fracture. The predictive model will then be validated in the group of patients included in the RNFC in 2021-2022.


The main goal of ABACO (Automatic Bed Assistance based on Continuous Optimization) project — developed in collaboration with Pixelabs S.L — is to develop, by using techniques and methods of Data Science, an intelligent algorithm that is able to learn dynamically by using several sleep session reports from smart-mattress and find optimal-pressures patters depending on the characteristics of the user. Thus, it is possible to know which parameters need to be changed to improve the experience, the comfort, and the quality of the sleep.


Internet of Things (IoT) to preserve the intensive livestock farming. This project is coordinated by the Unión de Pequeños Agricultores (UPA). The aim is to prevent wolf attacks using the analysis of live data taken from livestock holdings. These data are collected by digital-cowbells — developed by Digitanimal — that allow to track the position and monitoring the animals.


Development of the Health Sufficiency Indicator (HSI) during the COVID-19 pandemic. Visualization tool for better understanding of the stress of the Spanish Health System.

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SABERMED (Swarm Agent-Based Enviroment for Reputation in MEDicine) is a tool capable of assessing the reputation of digital content on the web, which will detect fraudulent content through the application of techniques of Data Science, Big Data architectures and Artificial Intellligent (Deep Learning and Intelligent Agents). SABERMED will incorporate data consolidation techniques, pattern detection, intelligent agents, decision support, visualization and representation of information, as well as advanced Big Data architectures that maximize system efficiency by optimizing the infrastructure and associated resources. 2018-2021.

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KROSS: Knowledge Recovering and Organization Semantic System

KROOS is a project funded by MedLab Media Group for the management and knowledge recovering of scientific documents. Its main objectives are:

  • To obtain the relevance of documents, phrases, concepts, words...
  • Monitoring the dynamic evolution of relevances.
  • Obtaining trends.
  • Calculating summaries.
  • Text generation.


Modeling the behavior of cattle using Data Science techniques and IoT and Big Data tools. 2018-2021.

  • Development of Machine Learning algorithms to automate the detection of abnormal behavior in cattle.
  • Creation of new use cases for the application of Data Science techniques in the cattle domain.
  • Development of new metrics for training and validation of Machine Learning algorithms.
  • Creation of software that facilitates the scaling of new services through Big Data technologies.

Machine Learning & Security

CAM project. Where Machine Learning and Security meet each other. 2018-2020.

Data Science United

DSUnited. DSLab project to allow data scientists and data engineers meet each other.

PPI. Protección de la Propiedad Intelectual

Development of a tool capable of tracking the presence of a certain DigitAnimalmaterial imitating the behavior of a user of fraudulent content. 2016-2018.

Unified Knowledge: UNIKO

A social research support platform that increases the efficiency of research, providing researchers with a tool that makes it as easy as possible to search and process input data, covering all possible sources: from other research to direct feedback from people in society in general or the research community. 2016-2018.

Invisum: INtelligent VideoSUveillance SysteM

NVISUM is a project funded by the Spanish Ministry of Economy and Competitivity focused on the development of an advanced and complete security system. The goal of the project is the development of an intelligent video surveillance system that addresses the limitations of scalability and flexibility of current video surveillance systems incorporating new compression techniques, pattern detection, decision support, and advanced architectures to maximize the efficiency of the system. 2014-2017.


DRACO (Dynamic Recover and Automatic Communities Organizer) is a project that will provide a software tool capable of dynamically modeling social behavior and automatically detect the communities in which individuals are organized according to their behavior. For this purpose, techniques for the recovery and organization of information, Data Science and Machine Learning will be used. 2019-2022.


EMOBRAND (System for the Visualization of Brand Emotion from Multiple Views) is a tool able to evaluate and provide a visualization about the image of a brand on the network, as well as the relation of the image with the brand identity. Techniques for the recovery and organization of information, Data Science, Machine Learning, Artificial Intelligence, Text Processing and Computer Vision will be used. 2019-2022.


A prototype of a new tool for dynamic sentiment analysis of textual content from websites. This prototype includes a visual and dynamic framework to analyze texts, based on a well-established lexicon. An unsupervised learning algorithm can append new words and calculate or update their sentiment polarization and strength over time. Moreover, it can increase the number of words considered for sentiment analysis to improve the accuracy of results. A novel dynamic visualization module makes it easier for end users to interpret sentiments associated to terms and their changes. 2018-2019.