(Despacho de Asesores del Vicerrectorado de Estudiantes, Ed. Estudiantes, Ciudad Universitaria)

Bio

Associate Professor (Profesora Titular) in UCM since 2023. PhD in Applied Mathematics (2011). Visiting scholar at Max Planck Institute from 2005-2006 and at Laboratorio sui Sistemi Complessi della Scuola Superiore di Catania from 2007-2008. From 2013-2014 she was senior researcher at Commissariat à l'énergie atomique et aux énergies alternatives in Paris. She has participated in many different research founded projects.

Teaching

Profesora excelente periodo curso 2019/20-2220/21-2021/22

Research interests

Mathematical Modelling, Big data, Applied Mathematics, Complex Systems, Opinion Prediction. Higher Education and TIC’s.

Publications: Google Scholar, ResearchGate, Scopus, ORCID, Producción Científica UCM

Latest Publications

• A. B. Kubik,  M. R. Ferrández, M. Vela-Pérez, B. Ivorra,  A. M. Ramos, Modelling the COVID-19 pandemic: variants and vaccines. In Proceedings of The 8th European Congress on Computational Methods in Applied Sciences and Engineering ECCOMAS Congress 2022, 5-9 June 2022, Oslo, Norway. https://www.scipedia.com/public/Kubik_et_al_2022a
• A.M. Ramos, M.R.  Ferrández, M. Vela-Pérez, A.B.  Kubik, B. Ivorra. A simple but complex enough θ-SIR type model to be used with COVID-19 real data. Application to the case of Italy: Nonlinear Phenomena, 2021, 421, 132839. https://doi.org/10.1016/j.physd.2020.132839
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  A.M. Ramos, M. Vela-Pérez, M.R.  Ferrández, A.B. Kubik, B. Ivorra. Modeling the impact of SARS-CoV-2 variants and vaccines on the spread of COVID-19. Communications in Nonlinear Science and Numerical Simulation 2021, 102, 105937. https://doi.org/10.1016/j.cnsns.2021.105937

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  B.Ivorra, M.R. Ferrández, M. Vela-Pérez, A.M. Ramos. Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China. Communications in Nonlinear Science and Numerical Simulation, 2020, 88, 105303. https://doi.org/10.1016/j.cnsns.2020.105303

• A. M. Ramos, M.R. Ferrández, M. Vela-Pérez, A. B. Kubik and B. Ivorra. A simple but complex enough θthetaθ-SIR type model to be used with COVID-19 real data. Application to the case of Italy. Accepted for publication in Physica D: Nonlinear Phenomena. DOI link: https://doi.org/10.1016/j.physd.2020.132839. Research Gate Preprint, 2020. DOI link: https://doi.org/10.13140/RG.2.2.32466.17601
• E. García-Cuesta, D. Gómez-Vergel, L. Gracia-Expósito, J. M. López-López, M. Vela-Pérez. Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods. Appl. Sci. 2020, 10(12), 4196. https://doi.org/10.3390/app10124196
• García-Cuesta, D. Gómez-Vergel, L. Gracia-Expósito, J.M. López-López, M. Vela-Pérez. Prediction of opinion keywords and their sentiment strength score using latent space learning methods.  Applied Sciences (Switzerland), 2020, 10(12), 4196. https://doi.org/10.3390/app10124196
• M. Bodnar, M. Vela Pérez. Mathematical and numerical analysis of low-grade gliomas model and the effects of chemotherapy. Communications in Nonlinear Science and Numerical Simulation. Volume 72, 2019, Pages 552-564. https://doi.org/10.1016/j.cnsns.2019.01.015