DISCIPLINARY BACKGROUND


Why study the PhD in Computer Science?


The PhD in Computer Science provides solutions to various problems of the environment through research and innovative application of knowledge in data mining, digital image and signal processing, intelligent computing, educational computing, data science, machine learning, among other areas of computer science.

The graduate has the disciplinary and methodological principles that allow him/her to independently generate and apply innovative knowledge.



Generate frontier knowledge in the field of computational sciences and apply it in an innovative way to the solution of environmental problems.
To be recognized as one of the best research programs in computational science at the national and international level.
General Objective

To train researchers who are capable of generating frontier knowledge and applying it in an innovative way to solve environmental problems by teaching courses, organizing research seminars, writing theses, writing articles and transferring knowledge to the productive and social sectors.

  • To train independent researchers in the area of computational sciences.
  • Delivering high quality courses
  • Organize events for the free academic discussion of ideas and projects.
  • To elaborate thesis following high quality standards
  • Publish scientific articles in high impact international journals
  • Transfer innovative knowledge to the productive and social sectors.
  • Intelligent Computing: Encompasses the study of artificial intelligence, computational intelligence, machine learning, pattern recognition, data mining, as well as the field of data science.
  • Educational computing: Studies the use and development of computer technologies and methods applied to teaching in modern learning environments.
  • Image and signal processing: Refers to the development and application of mathematical and computational methods to characterize electrical signals and digital images, applied in fields such as medicine, telecommunications, audio, video, among others.



Duration

4 YEARS


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Basic Academic Core

We have 10 research professors with doctoral degrees.



  • Jorge Enrique Lorenzo Rivasa, Heydy Castillejos Fernandez. Analysis and development of a solution oriented to cellular networks with emphasis on automotive safety and the Internet of Everything. Padi Semiannual Publication Vol. 7 No.14, pp. 23-29, 2020.
  • López-Ortega, Omar; Pérez-Cortés, Obed, Castillejos-Fernández. Heydy; Castro-Espinoza, Félix; González-Mendoza, Miguel. Written documents analyzed as nature-inspired processes: persistence, anti-persistence and random walks. APPLIED SCIENCES. 2020.
  • Àngela Nebot, Francisco Mugica and Félix Castro; An e-Learning toolbox based on rule-based fuzzy approaches. APPLIED SCIENCES. 2020.
  • O. Alonso-Hernández, Julio C. Ramos-Fernández, M. A. Márquez-Vera, Virgilio López-Morales, J. A. Ruiz-Vanoye, Joel Suárez-Cansino, Francisco R.Trejo-Macotela. Fuzzy infrared sensor for liquid level measurement: A multi-model approach.
  • Márquez-Vera, Marco Antonio; López-Ortega, Omar; Ramos-Velasco, Luis Enrique. Fault diagnosis using an LTSM and an elastic network. Revista Iberoamericana de Automática e informática industrial. 2021.
  • García-Islas, L.H., Franco-Arcega, A., Quiroz-Gutierrez, J.A and Franco-Sánchez, K.D. Frequent pattern mining for the testing of Susumu Ohno's rules and their use in the identificacioń of evolution of organisms. Pädi Scientific Bulletin of Basic and Engineering Sciences of ICBI, Vol 7, No. 13, pp. 84-89, 2019.
  • Herrera-Alcántara, O., Barrera-Animas, A.Y., González-Mendoza, M., Castro-Espinoza, F. Monitoring student activities with smartwatches: On the academic performance enhancement. Sensors (Switzerland) 19(7), 1605, 2019.
  • Juan Carlos Gonzalez-Islas, Omar Arturo Dominguez-Ramirez, Omar Lopez- Ortega, Gildardo Godinez-Garrido, Heydy Castillejos-Fernandez. A Proposal for Human Gait Analysis, 4th Pan-American Interdisciplinary Conference (PIC), 06-08 November, Mexico City, Mexico, Proceedings, pp 138, 2019.
  • López-Ortega, O., Castro-Espinoza, F., Fuzzy similarity metrics and their application to consensus reaching in group decision making. Journal of Intelligent and Fuzzy Systems, 36 (4), pp. 3095-3104, 2019.
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Dr. Omar López Ortega

Dr. Omar López Ortega
lopezo@uaeh.edu.mx
SNI I
PRODEP: Current
LGAC: Intelligent Computing

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Dr. María de los Ángeles Alonso Lavernia

Dr. María de los Ángeles Alonso Lavernia
marial@uaeh.edu.mx

PRODEP: Current
LGAC: Educational Computing

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Dr. Heberto Gómez Pozos

Dr. Heberto Gómez Pozos
gpozos@uaeh.edu.mx
SNI: II
PRODEP: Current
LGAC: Image and Signal Processing

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Dr. Heydy Castillejos Fernándeza

Dr. Heydy Castillejos Fernández
heydy_castillejos@uaeh.edu.mx
PRODEP: Current
LGAC: Image and Signal Processing


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Dr. Felix Agustin Castro Espinoza

Dr. Felix Agustin Castro Espinoza
fcastro@uaeh.edu.mx
SNI: I
PRODEP: Current
LGAC: Intelligent Computing

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Dr. Anilú Franco Árcega

Dr. Anilú Franco Árcega
afranco@uaeh.edu.mx
PRODEP: Current
LGAC: Intelligent Computing

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Dr. Omar Arturo Domínguez Ramírez

Dr. Omar Arturo Dominguez Ramirez
omar@uaeh.edu.mx
SNI I
PRODEP: Current
LGAC: Image and Signal Processing

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Dr. Virgilio López Morales

Dr. Virgilio López Morales
virgilio@uaeh.edu.mx
SNI I
PRODEP: Current
LGAC: Intelligent Computing


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Dr. Obed Pérez Cortés

Dr. Obed Pérez Cortés
obed_peres@uaeh.edu.mx
SNI I
PRODEP: Current
LGAC: Image and Signal Processing

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Dr. Joel Suárez Cansino

Dr. Joel Suárez Cansino
jsuarez@uaeh.edu.mx
PRODEP: Current
LGAC: Educational Computing










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