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.
Google Scholar 
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
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. Mathematics, 10(19), 3412, 2022
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Using Free Mathematical Software in Engineering Classes. Gayoso Martínez, V., Hernández Encinas, L., Martín Muñoz, A., & Queiruga Dios, A. Axioms, 10(4), 253. 2021
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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
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A general model of population dynamics accounting for multiple kinds of interaction. L Stucchi, JM Pastor, J García-Algarra, J Galeano. Complexity 2020
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Reducing Trade Inequality: A Network-Based Assessment J Garcia-Algarra, GG Bengoechea, ML Mouronte-López. Complexity 2020
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A structural approach to disentangle the visualization of bipartite biological networks. J Garcia-Algarra, JM Pastor, ML Mouronte, J Galeano. Complexity 2018
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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
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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).
- Javier García Algarra (Investigador)
- Pedro Concejero Cerezo (Investigador)
- Marcos Novalbos Mendiguchía (investigador)
