Program Overview
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Given UDLAP commitment to academic excellence, Knowledge, acquired by students, is expected to be of high-level, deep, significant, critical, innovative and socially relevant, that allows them to solve effectively professional problems, both theoretical and practical, leaving behind superficial, mechanical and non-reflective learning.

The Ph. D. program in Intelligent Systems aims to train researchers with the ability to design and implement innovative systems based on Artificial Intelligence, and to develop new technologies with leadership and ethics. These systems will be applied to different areas, such as Medical Signal Analysis, Communications, Automation, etc. These results will be attractive and beneficial to industrial sectors and society. This will be possible through the full-time student involvement in the program, research projects, groups associated with the research areas, publishing of results, dissemination of research activities in international forums, and engagement in continuous learning.

  • To promote the Ph. D. program in order to recruit candidates of high academic standards in the selection process.
  • To increase the number of publications by the faculty, who integrate the program, through the collaboration of doctoral students.
  • To link the Ph. D. program with companies, and with other national and international universities.
  • To contribute to the generation of new knowledge on different areas through the development of thesis projects by PhD students.
  • To train professionals with skills to do research so that they can work in companies or higher education institutions.

The student who wishes to enter is required a bachelor degree in areas related to the doctoral curriculum (areas of engineering, physical science - mathematics, computer science or equivalent). The student must take a general knowledge test as well as an English test that proves the level of proficiency in the language.

A graduate from the Ph. D. program on Intelligent Systems program is a professional trained to carry out research work. He/She is an expert and leader in his field, dynamic and competitive, prepared to respond to the changing landscape of information technology. The graduate is a high-level professional, prepared for exercising tasks of design, implementation and evaluation of AI systems. He/she is also trained to develop cutting-edge technology and to participate in multidisciplinary projects.

At the end of the program the student will:

  • Know methodologies for selection and review of previous research work through the collection of the latest research literature to identify the background for a research projects.
  • Master the procedures for preparation, development and publication of an article to deliver research results.
  • Plan, develop and carry out proposed executive proposals for research and technological development seeking financial efficiency and emerging technological trends to conduct research of high scientific, technological and socio-economic impact.
  • Collaborate and be part of research networks through the use of platforms, technology and protocols to enrich experience by criticizing or assimilating research from other academic peers.
  • Dedicate efforts to the completion of a research project through the interaction with colleagues and academic peers to generate products of scientific and technological impact, publications and talks at conferences.


Report and suitability of full-time teachers by research line

  • Signal Processing. The four professors, who participate in this line, direct doctoral theses, teach courses, and publish in areas of interest related to the research line. Research is carried out in the following areas of interest: Digital signal processing, computer vision, optical communications, security in communications networks, prediction, signals and systems, extraction of characteristics.

    1. Dr. Vicente Alarcón Aquino - Areas of interest: Cybersecurity, network monitoring, anomaly detection, wavelets and multiresolution analysis Research projects: Detection of anomalies in computational networks by applying immunity theory to danger. Object recognition through multi-resolution analysis in infra-red images for defense systems. Video compression using the Wavelet and Fovea transform. Classification of epileptic seizures in EEG signals using Wavelets-based neural networks. Prediction and detection of forest fires using multiresolution optimization. EEG signal processing for brain-computer interface development. Security in communications networks through biometric systems. Detection and prediction of anomalies in computational networks using Wavelet transform. Thesis direction of three students. Courses taught: Advanced Elective (Select Topics – Multi Resolution Processing), Thesis, Knowledge Exam.
    2. Dr. Pedro Bañuelos Sánchez - Areas of interest: Correction of the power factor, Electric power quality, power converters for unconventional power sources, photovoltaic power and wind power, Smart Grid, non-contact electric power transmission for electric traction application, multilevel converters, multilevel rectifiers, soft switching CD/CD converters, undymotive power, power sources for high-intensity LEDs. Dr. Bañuelos is the author of two patents: one, on the design of an electric power generator from the waves of the sea; and the second, by a solar-plowed tract. Both patents have been granted by the Mexican Institute of Industrial Property (IMPI).
    3. Dr. Jorge Rodríguez Asomoza - Areas of interest: Optoelectronics for the sensing of electrical signals, high capacity optical communications systems and optical fiber devices with biomedical applications. Adjusting bandwidth in micro-wave signals. Adjusting the range of a notch filter in fiber optics by adjusting the optical link length. Photonic filters in microwaves. A student's thesis direction. Courses taught: Research Seminar, Advanced Elective (Select Topics – Optical Communications), Pre-doctoral Examination.
    4. Dr. Oleg Starostenko Basarab - Areas of interest: Multimedia Information Access, Transmission, Recovery and Processing Systems. Research projects: Extract people in images with green screen in the background. Recognizing facial expressions using local appearance descriptors. Algorithms for musical composition with artificial intelligence. QR code detection in uncontrolled environments. Decrypting text CAPTCHAS with variable character orientation. Nonverbal communication using wearable haptic technology. Music display. Methods for the generation of land in video games. Thesis direction of three students. Co-direction of a student's thesis. Courses taught: Research Seminar, Artificial Vision, Multi-medium Signal Processing.
  • Biomedical Systems. The four professors, who participate in this line, direct doctoral theses, teach courses, and publish in areas of interest related to the research line. Research is carried out in the following areas of interest: Recognition of patterns in biomedical signals, analysis of medical images, brain-computer interfaces, imaginology, biophotonics.

    1. Dr. Juan Horacio Espinoza Rodríguez - Areas of interest: Monte Carlo simulation for light excitation tissue analysis, machine learning application for protein interaction analysis, in vitro irradiation of sensitive molecules to light for therapeutic purposes. A student's thesis direction. Courses taught: Modeling dynamic systems.
    2. Dr. Gibrán Etcheverry Doger - Areas of interest: Feature extraction in biomedical signals, stochastic processes, time series prediction, modeling of nonlinear. Thesis direction of two students. Courses taught: Soft Computing, Basic Elective I (Statistical Learning Methods), Basic Elective II (Heuristic Computer Methods), Research Seminar.
    3. Dr. Roberto Rosas Romero - Areas of interest: Recognition of patterns in biomedical signals, processing of biomedical signals, analysis of medical images, extraction and classification of traits of biomedical signals for disease diagnosis. Website: Dr. Rosas has worked on the detection of endocardial in ultrasound images, detection of micro-aneurysms in eye background images to assist in the diagnosis of diabetic retinopathy, prediction of epileptic seizures by classifying electroencephalogram (EEG) signals and near-red spectrum (fNIRS) with convolutional networks and machine learning, detection of micro-calcifications in X-ray images for assistance during breast cancer diagnosis, voice trait analysis for classification within early stage Parkinson's disease detection, prediction of time series in finance (stock exchange), extraction of people in green-screen images in the background, video analysis for remote detection of wildfires , recognition of video actions through video processing and machine learning. Address of a student's thesis and co-address of a student. Courses taught: Optimization Techniques, Pattern Recognition, Basic Elective I (Statistical Learning Methods), Soft Computing, Research Seminar.
    4. Dr. Rocío Salazar Varas - Areas of interest: Biomedical signal processing, electroencephalography signals, brain-computer interfaces. Website: Research projects: EEG signals, neural analysis of visual stimuli. Thesis direction of a student and co-direction of thesis of a student. Courses taught: Pattern Recognition, Advanced Elective (Bio-inspired Hardware).
  • Artificial Intelligence. The four teachers, who participate in this line, carry out projects, teach courses, and publish in areas of interest related to the research line. Research is carried out in the following areas of interest: Heuristic algorithms, artificial intelligence, machine learning, convex optimization, whole and combinatorial programming, intelligent agents, human-computer interaction:

    1. Dr. Juan Antonio Díaz García - Areas of interest: Accurate and heuristic algorithms for complete programming and combinatorial problems related to logistics and manufacturing systems. Direction of a student's thesis. Courses taught: Basic Elective II (Heuristic Learning Methods).
    2. Dr. Zobeida Jezabel Guzmán Zavaleta - Digital content analysis, computational vision, image processing, video processing, watermarks, artificial learning, deep learning. Thesis direction of a student and co-direction of thesis of a student. Courses taught: Thesis, Advanced Elective (Select Topics - Video Processing).
    3. Dr. Mireya Paredes López - Areas of interest: Quantum Computing, Artificial Intelligence. Recent addition. Courses taught: Artificial Intelligence, Thesis.
    4. Dr. Miguel Ángel Reyes Cortés - Works on topics related to compositional data and functional regression. His lines of inquiry are varied. He has had collaborations in which he has applied novel statistical techniques in biological and agro-ecological sciences, has participated in population studies projects related to migration and population aging, as well as multidisciplinary projects in which the application and research of statistical techniques has been fundamental. In this sense, his main research in statistics is the non-parametric estimation of curves.

Other topics of interest

Note: offering of the program is subject to the number of students enrolled.

For more information contact:

Research and Graduate Studies Office
Tel.: +52 222 229 20 00 ext. 2725 and 6005

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