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.
On November 12th, I got the position of Full Professor at the Dept. of Applied Mathematics of UPV. It has been a long and winding trip of almost 22 years since I started my PhD. I did not think about this when I started. I did not dream of it. I neither consider it as the measure of academic success.
After getting the tenure position in 2009, I did not think about it for some time. However, 8 years ago I simply thought one day: Maybe I can get it, why not? Essentially, it is to keep on working day after day and getting more results. And I started to keep on working even at night or before dawn. Later, three years ago in the lobby of a hotel in Boston, reading the new criteria for becoming a full professor I thought, I absolutely will get it.
Many people have asked me if I am happier or if I have celebrated. Truth be told that happiness only lasted some days, later you return to your daily tasks and almost forget it. Besides, celebrations are very limited in these strange times, so almost anything has changed in my life after it.
Does it worth all the time invested? I honestly do not know. I enjoyed quite a lot during the path, but for the things you meet along the way, not because of the position, and to be focused on the path you missed many things. You can never have it all and you have to decide.
We Alberto Conejero (Instituto Universitario de Matemática Pura y Aplicada, IUMPA) and Miguel Rebollo (of the Valencian Research Institute for Artificial Intelligence, VRAIN) of UPV are part of the Data Science Working Group in the Fight against COVID-19 , of the Commissioner for the Presidency of the Generalitat Valenciana on Strategy for Artificial Intelligence and Data Sciences against COVID-19. News appeared in UPV, in El Periodic newspaper and in RUVID website.
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
