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Computer Science and Technology

  • Inicio

    Director

    Prof. Jesús Carretero Pérez
    Department of Computer Science and Engineering

    About the program

    The main objective of this program is to train researchers with advanced scientific and technological knowledge and who are qualified for research and innovation in the field of Information Technology. Specific research topics are related to the fields of Software Engineering, Artificial Intelligence, and Distributed, secure and multimedia systems.

    • Program regulated by RD 99/2011, January 28

    Department

     

    Computer Science and Engineering Department

     

     

    Doctorado en Ciencia y Tecnología Informática
  • ACCESS

    Student profile

    The program is aimed at students with a postgraduate background in Computer Sciences Engineering.

    Candidates with a different background may be considered for the program as long as they comply with the requirements established by current regulation regarding access to PhD studies. In these cases, the Academic Committee will determine the complementary training that candidates will need to complete.

    It is required that students have a level of proficiency in English that is enough to allow reading and writing international papers as well as attendance and participation in international conferences and events.

    Admission requirements

    According to art. 6 of the PhD studies regulation (RD 99/2011), in order to access the Program it is required to have a Bachelor's degree (or equivalent) and a Master's degree (or equivalent), provided that at least 300 ECTS credits have been passed in these two cycles as a whole, or the equivalent degree qualifies for level 3 of MECES (Marco Español de Cualificaciones para la Educación Superior, Spanish Framework for Higher Education Qualifications).

    Likewise, access is available to candidates in possession of foreign degrees from countries integrated into the European Higher Education Area (EHEA) when the degree can be accredited as level 7 in the European Qualifications Framework (EQF), as long as the aforementioned degree allows access to PhD level studies in the country of expedition; and candidates with a degree which is equivalent to a Spanish Master's degree, obtained in foreign education systems outside the EHEA, as long as the aforementioned degree allows access to PhD level studies in the country of expedition.

    Admission criteria

     

    • Adequacy of candidate's profile and previous studies to the lines of research of the program (40%)
    • Academic background and curriculum vitae (40%)
    • Motivation and committment to the program, research interests and letters of recommendation (20%)

     

    Seats available for the academic year: 25

     

     

  • FACULTY
  • TRAINING

    In addition to the elaboration of the doctoral thesis, students must take some training aimed at improving their research skills as well as ensuring the scientific quality of their research work. This is structured in the following types of training.

    Specific training 

    • Research Seminars and Lectures: cycles organized by the department and involving relevant research topics in the area of Computer Science and are taught by prestigious visiting professors, mostly from foreign universities. This training will be conducted mostly in English. This takes place during the first semester of each academic year. Student dedication: 20 hours

      Further information on seminars
       
    • Presentation of research results, in which Ph.D. students will participate during their second year. During these meetings, which will last for 1 to 2 days, Ph.D. students will make presentations on the development state of their thesis to other Ph.D. students and researchers from the Department.
       
    • Publication of research work: preparation and writing of articles for scientific research in journals with a proven research profile, supported by its inclusion in international databases of scientific nature. The selected journal must meet standards accepted by the international scientific community, such as anonymous peer review and should have an impact factor published in the JCR. Before depositing the thesis, each Ph.D. student must have a minimum of a research paper published or accepted for publication in one of these magazines. This publication must be made during the second or third year.
       
    • International research visit. This activity is optional for students. Ph.D. candidates, who have completed visits of at least 3 months in prestigious foreign institutions or research centers may apply for the international mention.
       
    • Ph.D. thesis pre-defense. This activity will be undertaken by students with an advanced stage of completion of their thesis, but before completion. The Academic Committee will appoint a panel of three doctors within the field of the thesis.

      The pre-reading committee will be made up of at least one Ph.D. form the relevant department and one external. The committee will issue its recommendations report.

    Research skills training

    Research skills training is focused on abilities common to all disciplines for the development of scientific and educational skills and the improvement of the professional career. This training consists of different activities (short courses, seminars, etc.), which can be recommended by the Academic Committee of the program.

    Students must take 6 credits (60 hours approximately) throughout the doctoral training period.

    Further information:

  • RESEARCH
    • Research Lines

      Interactive systems

      • Interactive and collaborative systems
      • ICT for emergency management and crisis informatic
      • Technology applied to education
      • Web Information Systems
      • Virtual and augmented reality
      • Biometry

      Knowledge Reuse

      • Knowledge representation
      • Knowledge retrieval
      • Knowledge reuse
      • Advanced methods for software development
      • Technologies to manage teams and products
      • Innovation, creativity and technological entrepreneurship
      • Processes management for reuse
      • New digital technologies to envision, develop manage and deploy software based systems
      • ITC Project Management
      • Business information systems
      • IT Government
      • Software engineering economics
      • Applied systems thinking

      Architecture of computers, communications and systems (ARCOS)

      • High performance computing
      • Parallel and distributed file systems
      • Real time systems
      • Sensors networks
      • Distributed systems
      • Infrastructure to facilitate BigData
      • Computer systems for biocomputing

      Computer Security Lab (COSEC)

      • Applied cryptography
      • Systems, software and network security
      • Privacy

      Planning and Learning

      • Automated Planning
      • Machine learning
      • Artificial Intelligence
      • Optimization
      • Heuristic search
      • Cognitive robotics
      • AReinforcement learning

      Control, Learning and Optimizationn

      • Machine learning
      • Artificial Intelligence
      • Data Analysis
      • Optimization
      • Predictive control
      • Pattern recognition
      • Multiagent systems
      • Data mining
      • Cognitive robotics

      Applied Artificial Intelligence (GIAA)

      • Machine learning
      • Data fusion
      • Active vision
      • Surveillance systems
      • Multiagent systems
      • Reputation management
      • Multiobjective optimization
      • Ambient Intelligence
      • Autonomous navigation systems
      • Fuzzy logic in control systems
      • Machine learning for spatio-temporal data
      • Smart citiess

      Neural Networks and Bio-inspired Computation

      • Machine learning
      • Bio-Inspired computation: genetic algorithms, evolutionary strategies, genetic programming, swarm intelligence
      • Artificial neural networks
      • Hybrid evolutionary learning systems
      • Data mining
      • Metaheuristics

      Human Languages and Accesibility Technologies

      • Response search systems
      • Methodological frameworks for the development of accessible web applications
      • Natural language processing
      • Information and communications technology and accessibility
      • Speech processing and speaker recognition
      • Deep learning methods applied to natural language processing
      • Natural language processing and accessibility
      • Voice recognition
      • Software for education
      • Audio processing
      • Audiovisual accessibility

      GIGABD

      • Semantic Web Technologies
      • Soft Computing in Corporate Information Systems, Business processes integration
      • Deep Learning applications and data enrichment
      • Accessibility and comprehensibility of data
      • Infrastructure to facilitate BigData
      • Information Analytics
    • Scientific results

      Publications from the doctoral theses defended in the PhD program

      • Thesis: Creación de Experiencias de Realidad Aumentada Realistas por Usuarios Finales
        Author: Álvaro Montero Montes
        Publication: Zarraonandia, T., Díaz, P., Montero, Á., Aedo, I., & Onorati, T. (2019). Using a Google Glass-based Classroom Feedback System to improve students to teacher communication. IEEE Access, 7, 16837-16846.
        Quality index: JIF(2019) 3.74, posición 35 de 256 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q1, 30 citas
      • Thesis: Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments
        Author: Saúl Alonso Monsalve
        Publication: Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., ... & Chiriacescu, A. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102(9), 092003.
        Quality index: JIF(2020) 5.29, posición 6 de 29 en PHYSICS, PARTICLES & FIELDS Q1, 63 citas
      • Thesis: Modelo de correlación QoS-QoE en un ambiente de aprovisionamiento de servicio de telecomunicaciones OTT-Telco
        Author: Julián Andrés Caicedo Muñoz
        Publication: Caicedo-Munoz, J. A., Espino, A. L., Corrales, J. C., & Rendon, A. (2018). QoS-Classifier for VPN and Non-VPN traffic based on time-related features. Computer Networks, 144, 271-279.
        Quality index: JIF(2018) 3.03, posición 13 de 53 en COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Q1, 39 citas
      • Thesis: Robust GNSS Carrier Phase-based Position and Attitude Estimation Theory and Applications
        Author: Daniel Arias Medina
        Publication: Medina, D., Vilà-Valls, J., Hesselbarth, A., Ziebold, R., & García, J. (2020). On the recursive joint position and attitude determination in multi-antenna GNSS platforms. Remote Sensing, 12(12), 1955.
        Quality index: JIF(2020) 2.45, posición 14 de 30 en REMOTE SENSING Q2, 25 citas
      • Thesis: Methods and Techniques for analyzing human factors facets on drivers
        Author: Botyuu Oscar Sipele Siale
        Publication: Škrjanc, I., Andonovski, G., Ledezma, A., Sipele, O., Iglesias, J. A., & Sanchis, A. (2018). Evolving cloud-based system for the recognition of drivers’ actions. Expert systems with applications, 99, 231-238.
        Quality index: JIF(2018) 4.29, posición 24 de 134 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1, 49 citas
      • Thesis: Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance
        Author: Andrés León Suárez Cetrulo
        Publication: Alonso-Monsalve, S., Suárez-Cetrulo, A. L., Cervantes, A., & Quintana, D. (2020). Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications, 149, 113250.
        Quality index: JIF(2018) 6.94, posición 23 de 119 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1, 197 citas
      • Thesis: Aplicación de algoritmos de inteligencia artificial a la optimización de la calidad de requisitos mediante sugerencias automáticas de mejora
        Author: Daniel Adanza Dopazo
        Publication: Dopazo, D. A., Pelayo, V. M., & Fuster, G. G. (2021). An automatic methodology for the quality enhancement of requirements using genetic algorithms. Information and Software Technology, 140, 106696.
        Quality index: JIF(2021) 3.86, posición 21 de 110 en COMPUTER SCIENCE, SOFTWARE ENGINEERING Q1, 18 citas
      • Thesis: Cybersecurity Mechanisms Leveraging Sensorial Data
        Author: Luis Hernández Álvarez
        Publication: Hernández-Álvarez, L., de Fuentes, J. M., González-Manzano, L., & Hernández Encinas, L. (2020). Privacy-preserving sensor-based continuous authentication and user profiling: a review. Sensors, 21(1), 92.
        Quality index: JIF (2020) 3,57, posición 14 de 64 en INSTRUMENTS & INSTRUMENTATION Q1, 53 citas
      • Thesis: Weakly supervised Deep Learning for Natural Language Processing​​​​​​​
        Author: Cristobal Colón Ruiz
        Publication: Colón-Ruiz, C., & Segura-Bedmar, I. (2020). Comparing deep learning architectures for sentiment analysis on drug reviews. Journal of Biomedical Informatics, 110, 103539.
        Quality index: JIF(2020) 6.3, posición 13 de 111 en COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q1, 176 citas
      • Thesis: New techniques to build and manage agnostic workflows for the processing of digital products​​​​​​​
        Author: Dante Domizzi Sánchez Gallegos
        Publication: Sanchez-Gallegos, D. D., Gonzalez-Compean, J. L., Carretero, J., Marin-Castro, H. M., Tchernykh, A., & Montella, R. (2022). PuzzleMesh: A puzzle model to build mesh of agnostic services for edge-fog-cloud. IEEE Transactions on Services Computing, 16(2), 1334-1345.
        Quality index: JIF(2022) 8.1, posición 13 de 58 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q1, 9 citas
    • Scientific publications

      This is a sample of relevant faculty publications:

      Knowledge Reuse

      • Eito-Brun, Ricardo ; Amescua-Seco, Antonio. Developments in Aerospace Software Engineering practices for VSEs: An overview of the process requirements and practices of integrated Maturity models and Standards. Advances in Space Research, 68(7), 2988-2998. JIF(2020) 2.61, posición 10 de 34 en ENGINEERING, AEROSPACE Q2, 1 cita. DOI: https://doi.org/10.1016/j.asr.2021.05.026
      • Poza, J., Moreno, V., Fraga, A., & Álvarez-Rodríguez, J. M. (2021). Genetic Algorithms: A Practical Approach to Generate Textual Patterns for Requirements Authoring. Applied Sciences, 11(23), 11378. JIF(2021) 2.83, posición 39 de 92 en ENGINEERING, MULTIDISCIPLINARY Q2, 5 citas. DOI: https://doi.org/10.3390/app112311378
      • Dopazo, D. A., Pelayo, V. M., & Fuster, G. G. (2021). An automatic methodology for the quality enhancement of requirements using genetic algorithms. Information and Software Technology, 140, 106696. JIF(2021) 3.86, posición 21 de 110 en COMPUTER SCIENCE, SOFTWARE ENGINEERING Q1, 18 citas. DOI: https://doi.org/10.1016/j.infsof.2021.106696
      • Sanchez-Segura, M. I., González-Cruz, R., Medina-Dominguez, F., & Dugarte-Peña, G. L. (2022). Valuable business knowledge asset discovery by processing unstructured data. Sustainability, 14(20), 12971. JIF(2022) 3.9, posición 114 de 275 en ENVIRONMENTAL SCIENCES Q2, 7 citas. DOI: https://doi.org/10.3390/su142012971
      • Sanchez‐Segura, M. I., Medina‐Dominguez, F., de Amescua, A., & Dugarte‐Peña, G. L. (2021). Knowledge governance maturity assessment can help software engineers during the design of business digitalization projects. Journal of Software: Evolution and Process, 33(4), e2326.    JIF(2021) 1.84, posición 64 de 110 en COMPUTER SCIENCE, SOFTWARE ENGINEERING Q3, 16 citas. DOI: https://doi.org/10.1002/smr.2326

      Interactive systems

      • Zarraonandia, T., Díaz, P., Montero, Á., Aedo, I., & Onorati, T. (2019). Using a Google Glass-based Classroom Feedback System to improve students to teacher communication. IEEE Access, 7, 16837-16846. JIF(2019) 3.74, posición 35 de 256 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q1, 30 citas. DOI: 10.1109/ACCESS.2019.2893971
      • Santos-Torres, A., Zarraonandia, T., Díaz, P., Onorati, T., & Aedo, I. (2020). An empirical comparison of interaction styles for map interfaces in immersive virtual environments. Multimedia Tools and Applications, 79, 35717-35738. JIF(2020) 2.57, posición 22 de 110 en COMPUTER SCIENCE, THEORY & METHODS Q2, 12 citas. DOI: https://doi.org/10.1007/s11042-020-08709-9
      • Ley-Flores, J., Alshami, E., Singh, A., Bevilacqua, F., Bianchi-Berthouze, N., Deroy, O., & Tajadura-Jiménez, A. (2022). Effects of pitch and musical sounds on body-representations when moving with sound. Scientific reports, 12(1), 2676. JIF(2022) 4.6, posición 22 de 73 en MULTIDISCIPLINARY SCIENCES Q2, 33 citas. DOI: https://doi.org/10.1038/s41598-022-06210-x
      • Sánchez de Francisco, M., Díaz, P., Onorati, T., & Aedo, I. (2023). Connecting citizens with urban environments through an augmented reality pervasive game. Multimedia Tools and Applications, 82(9), 12939-12955. JIF(2022) 2.9, posición 95 de 250 en COMPUTER SCIENCE, THEORY & METHODS Q2, 2 citas. DOI: https://doi.org/10.1007/s11042-022-14055-9
      • Tajadura-Jimenez, A., Ley-Flores, J., Valdiviezo, O., Singh, A., Sanchez-Martin, M., Diaz Duran, J., & Márquez Segura, E. (2022, June). Exploring the Design Space for Body Transformation Wearables to Support Physical Activity through Sensitizing and Bodystorming. In Proceedings of the 8th International Conference on Movement and Computing (pp. 1-9). 10 citas. DOI: https://doi.org/10.1145/3537972.3538001

      Architecture of computers, communications and systems (ARCOS)

      • Declara, P. F., Pérez, D. H. C., Garcia-Blas, J., Vom Bruch, D., Garcia, J. D., & Neufeld, N. (2019). A parallel-computing algorithm for high-energy physics particle tracking and decoding using gpu architectures. IEEE Access, 7, 91612-91626. JIF(2019) 3.74, posición 35 de 256 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q1, 18 citas. DOI: https://doi.org/10.1109/ACCESS.2019.2927261
      • López-Gómez, J., Muñoz, J. F., del Rio Astorga, D., Dolz, M. F., & García, J. D. (2019). Exploring stream parallel patterns in distributed MPI environments. Parallel Computing, 84, 24-36. JIF(2019) 1.19, posición 69 de 108 en COMPUTER SCIENCE, THEORY & METHODS Q3, 11 citas. DOI: https://doi.org/10.1016/j.parco.2019.03.004
      • Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., ... & Chiriacescu, A. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102(9), 092003. JIF(2020) 5.29, posición 6 de 29 en PHYSICS, PARTICLES & FIELDS Q1, 63 citas. DOI: https://doi.org/10.1103/PhysRevD.102.092003
      • Cascajo, A., Singh, D. E., & Carretero, J. (2022). Limitless—light-weight monitoring tool for large scale systems. Microprocessors and Microsystems, 93, 104586. JIF(2022) 2.6, posición 27 de 54 en COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Q2, 13 citas. DOI: https://doi.org/10.1016/j.micpro.2022.104586
      • Lozano, S., Lugo, T., & Carretero, J. (2023). A Comprehensive Survey on the Use of Hypervisors in Safety-Critical Systems. IEEE Access, 11, 36244-36263. JIF(2022) 3.4, posición 87 de 250 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q2, 12 citas. DOI: 10.1109/ACCESS.2023.3264825
      • Martinez-Rendon, C., Camarmas-Alonso, D., Carretero, J., & Gonzalez-Compean, J. L. (2022). On the continuous contract verification using blockchain and real-time data. Cluster Computing, 25(3), 2179-2201.  JIF (2022) 4.4, posición 24 de 111 en Computer Science, Theory & methods Q1, 18 citas. DOI: https://doi.org/10.1007/s10586-021-03252-0
      • Almaaitah, N. O., Singh, D. E., Özden, T., & Carretero, J. (2024). Performance-driven scheduling for malleable workloads. The Journal of Supercomputing, 1-29. JIF(2023) 2.5, posición 28 de 59 en COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Q2, 1 cita. DOI: https://doi.org/10.21203/rs.3.rs-3361646/v1
      • Blázquez, E., & Tapiador, J. (2023). Kunai: A static analysis framework for Android apps. SoftwareX, 22, 101370. JIF(2023) 2.4, posición 54 de 132 en COMPUTER SCIENCE, SOFTWARE ENGINEERING Q2, 1 cita. DOI: https://doi.org/10.1016/j.softx.2023.101370
      • Sanchez-Gallegos, D. D., Gonzalez-Compean, J. L., Carretero, J., Marin-Castro, H. M., Tchernykh, A., & Montella, R. (2022). PuzzleMesh: A puzzle model to build mesh of agnostic services for edge-fog-cloud. IEEE Transactions on Services Computing, 16(2), 1334-1345. JIF(2022) 8.1, posición 13 de 58 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q1, 9 citas. DOI: https://doi.org/10.1109/TSC.2022.3175057

      Computer Security Lab (COSEC)

      • Ortiz-Martin, L., Picazo-Sanchez, P., & Peris-Lopez, P. (2020). Are the interpulse intervals of an ECG signal a good source of entropy? An in-depth entropy analysis based on NIST 800-90B recommendation. Future Generation Computer Systems, 105, 346-360. JIF (2020) 7.18, posición 7 de 110 en Computer Science, Theory&methods, 8 citas. DOI: https://doi.org/10.1016/j.future.2019.12.002
      • Avarez-Rodríguez, J. M., Mendieta, R., Cibrián, E., & Llorens, J. (2023). Towards a method to quantitatively measure toolchain interoperability in the engineering lifecycle: A case study of digital hardware design. Computer Standards & Interfaces, 86, 103744. JIF(2023) 2, posición 9 de 54 en COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Q1, 7 citas. DOI: https://doi.org/10.1016/j.csi.2023.103744
      • Rashed, M., & Suarez-Tangil, G. (2021). An analysis of android malware classification services. Sensors, 21(16), 5671. JIF (2020) 3.24, posición 19 de 64 en INSTRUMENTS & INSTRUMENTATION Q2, 7citas. DOI: https://doi.org/10.3390/s21165671
      • Querejeta-Azurmendi, I., Arroyo Guardeño, D., Hernández-Ardieta, J. L., & Hernández Encinas, L. (2020). Netvote: a strict-coercion resistance re-voting based internet voting scheme with linear filtering. Mathematics, 8(9), 1618. JIF(2020) 2.25, posición 234 de 330 en MATHEMATICS Q1, 8 citas. DOI: https://doi.org/10.3390/math8091618 
      • Fuster-Barceló, C., Peris-Lopez, P., & Camara, C. (2022). ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks. Neurocomputing, 506, 37-49. JIF(2022) 6, posición 41 de 145 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q2, 13 citas. DOI: https://doi.org/10.1016/j.neucom.2022.07.059 
      • Gimenez-Aguilar, M., De Fuentes, J. M., Gonzalez-Manzano, L., & Arroyo, D. (2021). Achieving cybersecurity in blockchain-based systems: A survey. Future Generation Computer Systems, 124, 91-118. JIF (2020) 7.3, posición 8 de 110 en Computer Science, Theory&methods, 76 citas. DOI: http://dx.doi.org/10.1016/j.future.2021.05.007
      • Cbrero-Holgueras, J., & Pastrana, S. (2023). Towards automated homomorphic encryption parameter selection with fuzzy logic and linear programming. Expert Systems with Applications, 229, 120460. JIF(2023) 7.5, posición 24 de 197 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1, 9 citas. DOI: https://doi.org/10.1016/j.eswa.2023.120460 
      • Hernández-Álvarez, L., de Fuentes, J. M., González-Manzano, L., & Hernández Encinas, L. (2020). Privacy-preserving sensor-based continuous authentication and user profiling: a review. Sensors, 21(1), 92.  JIF (2020) 3,57, posición 14 de 64 en INSTRUMENTS & INSTRUMENTATION Q1, 53 citas. DOI: https://doi.org/10.3390/s21010092 
      • Cibrián, E., Álvarez-Rodríguez, J. M., Mendieta, R., & Llorens, J. (2023). Towards a Method to Enable the Selection of Physical Models within the Systems Engineering Process: A Case Study with Simulink Models. Applied Sciences, 13(21), 11999. JIF(2023) 2.5, posición 44 de 181 en ENGINEERING, MULTIDISCIPLINARY Q1, 7 citas. DOI: https://doi.org/10.3390/app132111999 
      • Suárez López, D., Álvarez-Rodríguez, J. M., & Inteligencia Artificial Aplicada-Cardenas, M. (2023). Toward a Model to Evaluate Machine-Processing Quality in Scientific Documentation and Its Impact on Information Retrieval. Applied Sciences, 13(24), 13075. JIF(2023) 2.5, posición 44 de 181 en ENGINEERING, MULTIDISCIPLINARY Q1. DOI: https://doi.org/10.3390/app132413075 

      Planning and Learning

      • José Carlos Pulido, Cristina Suárez Mejías, José Carlos González, Álvaro Dueñas Ruiz, Patricia Ferrand Ferri, María Encarnación Martínez Sahuquillo, Carmen Echevarría Ruiz De Vargas, Pedro Infante-Cossio, Carlos Luis Parra Calderón, Fernando Fernández. A Socially Assistive Robotic Platform for Upper-Limb Rehabilitation: A Longitudinal Study with Pediatric Patients. IEEE Robotics and Automation Magazine, pp. 24 - 39. 2019. JIF (2020) 3,59, posición 8 de 28 en Robotics Q2, 52 citas. DOI: 10.1109/MRA.2019.2905231
      • Fuentetaja, R., García-Olaya, A., García, J., González, J. C., & Fernández, F. (2020). An automated planning model for hri: Use cases on social assistive robotics. Sensors, 20(22), 6520. JIF (2020) 3,57, posición 14 de 64 en INSTRUMENTS & INSTRUMENTATION Q1, 6 citas. DOI: https://doi.org/10.3390/s20226520
      • Pozanco, A., Fernández, S., & Borrajo, D. (2018). Learning-driven goal generation. AI Communications, 31(2), 137-150. JIF(2018) 0.76, posición 118 de 134 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q4. DOI: https://doi.org/10.3390/s20226520
      • Majadas, R., García, J., & Fernández, F. (2024). Clustering-based attack detection for adversarial reinforcement learning. Applied Intelligence, 54(3), 2631-2647. JIF(2023) 3.4, posición 76 de 197 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q2, 1 cita. DOI: https://doi.org/10.1007/s10489-024-05275-7
      • Pulido, J. C., Fuentetaja, R., García, E., García, M., Abuín, V., González, J. C., ... & Fernández, F. (2024). A gamified social robotics platform for intensive therapies in neurorehabilitation. Intelligent Service Robotics, 1-25. JIF(2023) 2.3, posición 26 de 46 en ROBOTICS Q3, 1 cita. DOI: https://doi.org/10.1007/s11370-024-00521-w

      Control, Learning and Optimization

      • Vargas-Arcila, A. M., Corrales, J. C., Sanchis, A., & Gallón, Á. R. (2021). Peripheral diagnosis for propagated network faults. Journal of Network and Systems Management, 29(2), 14. JIF(2021) 2.19, posición 119 de 164 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q3, 4 citas. DOI: https://doi.org/10.1007/s10922-020-09579-0
      • Caicedo-Munoz, J. A., Espino, A. L., Corrales, J. C., & Rendon, A. (2018). QoS-Classifier for VPN and Non-VPN traffic based on time-related features. Computer Networks, 144, 271-279. JIF(2018) 3.03, posición 13 de 53 en COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Q1, 39 citas. DOI: https://doi.org/10.1016/j.comnet.2018.08.008
      • Simmonds, J., Gómez, J. A., & Ledezma, A. (2024). Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation. Information, 15(8), 448. JIF(2024) 2.4, en Computer Science, Information Systems Q3, 1 cita. DOI: https://doi.org/10.3390/info15080448
      • Škrjanc, I., Andonovski, G., Ledezma, A., Sipele, O., Iglesias, J. A., & Sanchis, A. (2018). Evolving cloud-based system for the recognition of drivers’ actions. Expert systems with applications, 99, 231-238. JIF(2018) 4.29, posición 24 de 134 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1, 49 citas. DOI: https://doi.org/10.1016/j.eswa.2017.11.008
      • Alonso-Monsalve, S., Suárez-Cetrulo, A. L., Cervantes, A., & Quintana, D. (2020). Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications, 149, 113250. JIF(2018) 6.94, posición 23 de 119 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1, 197 citas. DOI: https://doi.org/10.1016/j.eswa.2020.113250

      Applied Artificial Intelligence (GIAA)

      • Sadjadi, E. N., Menhaj, M. B., Herrero, J. G., & Inteligencia Artificial Aplicada Lopez, J. M. (2019). How effective are smooth compositions in predictive control of TS fuzzy models?. International Journal of Fuzzy Systems, 21, 1669-1686          JIF (2019) 4.406, posición 29 de 137 en Computer Science, Artificial Intelligent (Q1), 12 citas. DOI: https://doi.org/10.1007/s40815-019-00676-0
      • Medina, D., Vilà-Valls, J., Hesselbarth, A., Ziebold, R., & García, J. (2020). On the recursive joint position and attitude determination in multi-antenna GNSS platforms. Remote Sensing, 12(12), 1955. JIF(2020) 2.45, posición 14 de 30 en REMOTE SENSING Q2, 25 citas. DOI: https://doi.org/10.3390/rs12121955Cana, J. P. L., Herrero, J. G., & Lopez, J. M. M. (2022). An approach to forecasting and filtering noise in dynamic systems using LSTM architectures. Neurocomputing, 500, 637-648. JIF(2022) 6, posición 41 de 145 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q2, 2 citas. DOI: https://doi.org/10.1016/j.neucom.2021.08.162
      • Iriz, J., Patricio, M. A., Berlanga, A., & Molina, J. M. (2023). CONEqNet: convolutional music equalizer network. Multimedia Tools and Applications, 82(3), 3911-3930.    JIF(2023) 3, posición 44 de 154 en COMPUTER SCIENCE, THEORY & METHODS Q2, 3 citas. DOI: https://doi.org/10.1007/s11042-022-12523-w

      Neural Networks and Bio-inspired Computation

      • E. Martin, A. Cervantes, Y.Saez, P. Isasi, (2020) IACS-HCSP: Improved ant colony optimization for large-scale home care scheduling problems, Expert systems with applications 142, ISSN 0957-4174. JIF (2021) 3.60, posición 21 De 137 En: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE (Q1). DOI: https://doi.org/10.1016/j.eswa.2019.112994
      • Y. Saez, P. Isasi, A. Baldominos. (2020). On the automated, evolutionary design of neural networks: past, present, and future. Neural Computing and Applications. 32, ISSN 0941-0643. JIF (2021) 5.606, posición 34 De 175 En: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE (Q1), 45 citas. DOI: https://doi.org/10.1007/s00521-019-04160-6
      • A Alcántara, IM Galván, R Aler (2022). Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks. Engineering Applications of Artificial Intelligence, 114, 105128. JIF (2022) 3.4, posición 25 De 145 En COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS (Q1), 29 citas. DOI: https://doi.org/10.1016/j.engappai.2022.10512
      • García-Cuesta, E., Aler, R., Pózo-Vázquez, D. D., & Galván, I. M. (2023). A combination of supervised dimensionality reduction and learning methods to forecast solar radiation. Applied Intelligence, 53(11), 13053-13066.               JIF(2023) 3.4, posición 76 de 197 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q2, 17 citas. DOI: https://doi.org/10.1007/s10489-022-04175-y
      • A. Suárez-Cetrulo, D. Quintana and A. Cervantes (2023). A Survey on Machine Learning for Recurring Concept Drifting Data Streams. Expert Systems with Applications, Vol. 213, pp. 1-17. JIF (2023) 8.5, 26 de 197 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE (Q1), 77 citas. DOI: https://doi.org/10.1016/j.eswa.2022.118934

      Human Languages and Accesibility Technologies

      • Revuelta P., Ortiz T., Lucía M. J., Ruiz-Mezcua B., Sánchez-Pena J.M. (2020). Limitations of standard accessible captioning of sounds and music for deaf and hard of hearing people: An EEG study. Frontiers in Integrative Neuroscience, vol. 14, Feb. 2020. JIF(2020) 3.21, posición 29 de 53 en BEHAVIORAL SCIENCES Q2, 23 citas. DOI: https://doi.org/10.3389/fnint.2020.00001
      • Colón-Ruiz, C., & Segura-Bedmar, I. (2020). Comparing deep learning architectures for sentiment analysis on drug reviews. Journal of Biomedical Informatics, 110, 103539. JIF(2020) 6.3, posición 13 de 111 en COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q1, 176 citas. DOI: https://doi.org/10.1016/j.jbi.2020.103539
      • López-Hernández, J. L., González-Carrasco, I., López-Cuadrado, J. L., & Ruiz-Mezcua, B. (2021). Framework for the classification of emotions in people with visual disabilities through brain signals. Frontiers in Neuroinformatics, 15, 642766. JIF(2021) 3.73, posición 16 de 57 en MATHEMATICAL & COMPUTATIONAL BIOLOGY Q2, 11 cita. DOI: https://doi.org/10.3389/fninf.2021.642766
      • Rodrigo Alarcón, Lourdes Moreno, Isabel Segura Bedmar, Paloma Martínez Fernández. (2019). Lexical simplification approach using easy-to-read resources. Procesamiento del Lenguaje Natural. 63, 95-102. Sociedad Española para el Procesamiento del Lenguaje Natural, 1989-7553. 2019
      • Jaber, A., & Martínez, P. (2022). Disambiguating clinical abbreviations using a one-fits-all classifier based on deep learning techniques. Methods of Information in Medicine, 61(S 01), e28-e34. JIF(2022) 1.7, posición 128 de 158 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q4, 20 citas. DOI: https://doi.org/10.1055/s-0042-1742388
      • Masiello-Ruiz, J. M., Ruiz-Mezcua, B., Martinez, P., & Gonzalez-Carrasco, I. (2023). Synchro-Sub, an adaptive multi-algorithm framework for real-time subtitling synchronisation of multi-type TV programmes. Computing, 105(7), 1467-1495. JIF (2023) 3.3, posición 37 de 144 en Computer Science, Theory & methods Q2, 4 citas. DOI: https://doi.org/10.1007/s00607-023-01156-y

      GIGABD

      • Iglesias, A., Viciana, R., Pérez-Lorenzo, J. M., Ting, K. L. H., Tudela, A., Marfil, R., ... & Bandera, J. P. (2024). The Town Crier: A Use-Case Design and Implementation for a Socially Assistive Robot in Retirement Homes. Robotics, 13(4), 61. JIF(2024) 2.9, en Mechanical Engineering Q1, 4 citas. DOI: https://doi.org/10.3390/robotics13040061
      • Elahi, E., Iglesias, A., & Morato, J. (2022). Readability of Non-Text Images on the World Wide Web (WWW). IEEE Access, 10, 116627-116634. JIF(2022) 3.9, posición 73 de 158 en COMPUTER SCIENCE, INFORMATION SYSTEMS Q2, 4 citas. DOI: https://doi.org/10.1109/access.2022.3218632
      • Lan Hing Ting, K., Voilmy, D., De Roll, Q., Iglesias, A., & Marfil, R. (2021). Fieldwork and field trials in hospitals: co-designing a robotic solution to support data collection in geriatric assessment. Applied Sciences, 11(7), 3046. JIF(2021) 2.83, posición 39 de 92 en ENGINEERING, MULTIDISCIPLINARY Q2, 7 citas. DOI: https://doi.org/10.3390/app11073046
      • Iglesias, A., García, J., Garcia-Olaya, A., Fuentetaja, R., Fernández, F., Romero-Garcés, A., ... & Suárez-Mejías, C. (2021). Extending the evaluation of social assistive robots with accessibility indicators: The AUSUS evaluation framework. IEEE Transactions on Human-Machine Systems, 51(6), 601-612. JIF(2021) 4.12, posición 60 de 145 en COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q2, 10 citas. DOI: https://doi.org/10.1109/THMS.2021.3112976

      Further information on the full list of publicationslistado completo de publicaciones

  • THESIS

    Preparation of the thesis

    Please check the Guide with recommendations that the University Library has accessible on its website.

    You must always follow the guidelines of your thesis advisor, the guidelines of your doctoral program, and the regulations of the Doctoral School.

    Thesis defense

    The doctoral thesis consists of an original research work developed by the doctoral student in the field of knowledge established by the program, which enables the student for autonomous work in the field of R+D+i.

    Universidad Carlos III de Madrid and its Doctoral School establish follow-up procedures to guarantee the quality of the student's training and supervision. They also facilitate the procedures for the proper evaluation and defense of the doctoral thesis.

    Further information: 

    Requirements for thesis defense in Computer Science and Technology

    Before initiating thesis defense proceedings, please:

    -Contact the Administration Office of the Department to learn about the specific stages of the defense process.

    -Check the Training and research activities required by the Ph.D. Program that have to be registered on your academic record prior to the defense.

    • Ph.D. thesis pre-defense: The pre-defense will take place at an advanced stage of the thesis, but before completion.

      The Academic Committee will appoint a panel of three doctors related to the topic of the thesis. The committee’s pre-reading will include at least a doctor from the Department and an external one. After the presentation of the thesis by the student, each member of the committee shall prepare a separate report, in which he will indicate the merits of the thesis as well as aspects that require improvement before its defense. The committee may also give a negative report and reject the thesis.
       
    • Thesis deposit: to submit his Ph.D. thesis, the student will be required to demonstrate his work by presenting his research merits before the Academic Committee.

    Thesis as a collection of publications

    The Academic Committee of the Ph.D. Program can authorize the presentation of the doctoral thesis as a collection of publications. These are required to have a thematic coherence and meet the requirements of originality, quality, quantity and authorship by the Ph.D. candidate as regulated by the Ph.D. Program in Computer Science and Technology. Further information: Thesis as a collection of publications

    The defense of these thesis will be carried out according to the same periods, appointment of Thesis Committees, mentions, etc. than those of a thesis presented in a regular format.

  • QUALITY

    GENERAL INFORMATION ABOUT PH.D.

    ☛ Implementation Year: 2013-2014

    QUALITY ASSURANCE

    The Academic Committee of the Ph.D. complies with the SGIC-UC3M. It is responsible for the quality analysis of the program and produces the Degree Reports ("Memoria Académica de Titulación").

    QUALITY INDICATORS

    COMPLAINTS AND SUGGESTIONS

  • CONTACT
    Bienvenida Universidad Carlos III de Madrid

    Doctoral School Office | Leganés Campus

    Rey Pastor Building, Office 3.0.B.08
    Avenida de la Universidad, 30
    28911 Leganés (Madrid)

    Contact