Viernes: 12:30-13:30

 

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