University of Technology, Arts and Design

DRACO

Research Group

Autor de la imagen

DRACO is a research group specializing in Data Engineering, formed by Software Engineering, Double Degree in Software Engineering + Mathematics and Double Degree in Physics faculty, focusing on Artificial Intelligence and Machine Learning applied to Data Science. With the massive generation and collection of data that currently exists in all areas of life, data science represents the most exciting and expanding field for current and future society, and is therefore prime for carrying out any research work.

Dra. Ramona Ruiz Blázquez

Research Group Director

Dr. Francisco Javier García Algarra

Dra. Beatriz Martínez Pabón

Dr. Alfonso Castro Escudero

Dr. Pablo Ramos Criado

Dr. Pedro Concejero Cerezo

Dra. Mariluz Congosto

Mar Angulo Martínez, Phd. Candidate

Manoel Fernando Alonso Gadi, Phd. Candidate

Emilio Mino Díaz, Ingeniero de Telecomunicación

Miguel Ángel Mesas, Estadístico

Dr. Miguel Ángel Muñoz Mohedano

Dr. Víctor Gayoso Martínez

Jaime Barahona Martínez, Phd. Candidate

DRACO Publications

Mathematics and Its Applications in Science and Engineering. Queiruga-Dios, Araceli and Santos Sánchez, María Jesus and Yilmaz, Fatih and Dias Rasteiro, Deolinda M. L. and Martín-Vaquero, Jesús and Gayoso Martínez, Víctor. Mathematics10(19), 3412, 2022

 

Using Free Mathematical Software in Engineering Classes.  Gayoso Martínez, V., Hernández Encinas, L., Martín Muñoz, A., & Queiruga Dios, A. Axioms, 10(4), 253. 2021

 

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble. I Civantos-Gómez, J García-Algarra, D García-Callejas, J Galeano. PLOS Computational Biology 17 (12), e1008906. 2021

 

A general model of population dynamics accounting for multiple kinds of interaction. L Stucchi, JM Pastor, J García-Algarra, J Galeano. Complexity 2020

 

Reducing Trade Inequality: A Network-Based Assessment

J Garcia-Algarra, GG Bengoechea, ML Mouronte-López. Complexity 2020

 

A structural approach to disentangle the visualization of bipartite biological networks. J Garcia-Algarra, JM Pastor, ML Mouronte, J Galeano.

Complexity 2018

 

Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition. J García-Algarra, JM Pastor, JM Iriondo, J Galeano. PeerJ 5, e3321. 2017

 

Research Projects

PREvEnT

PrevEnt (Pollinators Responses to Evolving Environmental Trends)

The purpose of this project is to make spatial predictions of pollinator abundance with convolutional neural networks, using image recognition, based on data from CropPol (a global and open database on crop pollination).

PREvENT team

  • Javier García Algarra (Investigador)
  • Pedro Concejero Cerezo (Investigador)
  • Marcos Novalbos Mendiguchía (investigador)