Analysis & Applied Math.
Linear dynamics. Semigroup Th. & Fractional Calculus with PDE's. Algebraic Structures in Analysis.
Research projects
This is an overview of the research topics in which I am involved.
Biomedical Data Science
Data Analysis of Biomedical Problems. Data Quality, Statistics, and Machine Learning.
Maths & Biology
Modelling of cyanobacteria cultures. Metabolic networks. Phylogenies.
Maths & Computer Science
Math modeling based on graph theory, numerical analysis, and information processing.
Maths & Computer Networks
Dissemination of messages in computer and mobile networks.
Maths & Ind. Engineering
Detection of failures in electric machines. Energy efficiency modeling.
Mentoring & Outreach
Ddevelopment of several mentoring and outreach programs
iGEM (Synthetic Biology)
Instructor at iGEM Valencia_UPV team.
Teaching
Courses, academic guided works, and other projects.
Last news
About my academic activity.
Since April 2020, I have been involved in the Data Science Group COVID-19 of the València Region under the supervision of Nuria Oliver. This is a multidisciplinary team of volunteers that work side by side with the General Director of Analysis and Public Policies of the Presidency of the RValencia Region Government. The analysis for COVID-19 is coordinated with the Ministry of Health and the rest of the Councils involved. This working group is led by Nuria Oliver, Commissioner of the Presidency of the Generalitat for the Valencian Strategy for Artificial Intelligence and, especially, for the coordination of data intelligence before the COVID-19 epidemic in the Valencia Region.
They are part of the group of experts from the Jaume I University, the University of Valencia, the Polytechnic University of Valencia, the Miguel Hernández University, the University of Alacant, the CEU Cardenal Herrera University, Fisabio, and Microsoft, with the collaboration of Esri, the INE, the Secretary of State for Artificial Intelligence and the three most important mobile phone companies in the country.
This group is divided into three priority areas with their respective work coordinators: (1) analysis, visualization, and modeling of mobility data, (2) epidemiological models and (3) data science applied to COVID-19. There, I work in epidemiological models with Antonio Falcó, Miguel Rebollo, Miguel A. Lozano, Emilio Sansano, Xavier Barber, and Francisco Escolano.
This is the web page of our data research group http://infocoronavirus.gva.es/es/grup-de-ciencies-de-dades-del-covid-19-de-la-comunitat-valenciana
The lack of representative COVID-19 data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, where source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. In this work, we used the publicly available nCov2019 dataset, including patient level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.
In our work published in JAMIA, we have shown that cases from the two countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting. We conclude that data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.
Our analysis tool developed within BDSLab at UPV can be found at http://covid19sdetool.upv.es/?tab=ncov2019
Within the context of the collaborative university project «Data Literacy in Context» (DaLiCo), the first summer school with international students as participants and lecturers from all partner countries of the project took place in the last week of September. It was organized by the University of Applied Sciences Hamburg (HAW Hamburg), in a team led by Prof. Christine Gläser (project coordinator, Department Information) with contributions from the partner universities Stichting Hogeschool Utrecht (Netherlands), University of Debrecen (Hungary), and Universitat Politècnica de València (Spain).
During the summer school with the topic of Open (Governmental) Data, the participants studied intensively in lectures and workshops the infrastructures of open data sources, data analysis and visualization as well as data ethical questions and concepts, and finally the aspects of data sharing in the research context.
In data projects, the international teams of PhD, Master and Bachelor students deepened their newly acquired knowledge and put it to practical use. Using the World Happiness Report, I mentored some groups that developed their own research questions and examined whether aspects such as gender equality or unemployment have an influence on the happiness index in different countries. The research results of these projects can be viewed on the project website projects.dalico.info
On September 30th, 2020 we have received funding for the project Ciencias de Datos e Inteligencia Artificial contra el COVID-19, IA4COVID19, from Fundación BBVA among more than 150 proposals presented to the call in the category: Big Data e Inteligencia Artificial (“Data-IA-COVID-19”). This proposal has been lead by the data research scientist Nuria Oliver from Ellis Alicante. The initiative, is linked to the Valencian Strategy in Artificial Intelligence.
Our project is a collaborative work that we have developed voluntarily and altruistically since the beginning of the crisis caused by the pandemic, professors from Valencian universities. The research entitled «Data science against Covid-19» brings together the participation of civil society (through a citizen survey), experts from the academic-research environment, and public administration, with the aim of providing information so that the those responsible for public crisis management can make informed decisions based on scientific evidence obtained from data analysis. In particular, I collaborate in the epidemiological models part and as head of the UPV node together with Miguel Rebollo from the VRAIN Institute. The initiative, linked to the Valencian Strategy in Artificial Intelligence through the commissioner of the presidency occupied by the researcher Nuria Oliver.
