Gilberto Ochoa Ruiz
Computing Science Engineering

Gilberto Ochoa Ruiz is a researcher in Computer Vision, Machine Learning and Internet of Things. He has participated as associated researcher and lecturer in several programs accredited by the CONACYT PNPC program, geared around Computer Science and Communication and Information Technologies. He is member of the Sistema Nacional de Investigadores (SNI, Rank I) and of the CONACYT Network on Applied Computational Network (RedICA), the Mexican Societies of IA (SMIA) and Computer Sc. (AMEXCOMP), as well as the Latinx in AI (LXAI) Coalition. He as served as reviewer for ICLR, ICML, CVPR and IJCNN and as general chair of the Latinx in Computer Vision Workshops at CVPR and ICCV and as part of the organization or technical program committes of these efforts, as well as other conferences.

He coursed a bachelor’s degree in Electronics and Communications Engineering by the Universidad de Guadalajara and a specialty degree in Digital and SoC Design at the CINVESTAV Guadalajara. Afterwards, he obtained a master’s degree in computer Vision and Robotics by Heriot-Watt-University (Edinburgh, UK) and a PhD in Electronic Imaging and Computer Vision by the Université de Bourgogne (France). Afterwards, he carried two post-doctoral positions at the Institut National de Recherche en Informatique et Automatique (Lille, France) and later at the CNRS laboratory Lab-STICC (Lorient, France). Since 2019, he is part of the academic staff of the Instituto Tecnológico de Estudios Superiores de Monterrey (ITESM), in its Guadalajara campus.

Research areas
  • Applied Artificial Intelligence (Computer vision and machine learning) for medical imaging
  • Applied Computational Intelligence, Smart Connected Devices, AI at the edge.
  • Optimization techniques for designing edge AI models and devices.

He is currently involved in the following projects: related with medical Imaging and digital pathology, win which we are interested in developing new image medical analysis and processing methods, but also machine learning models to facilitate the diagnosis of various diseases. We are also actively exploring the development of interpretability and explainability tools for deep learning models.

  • Assessing the applicability of deep learning and image analysis methods for improving the endoscopic identification of kidney stones composition

In collaboration with the Centre de Recherche en Automatique de Nancy, CRAN (France) and the Institut National de la Santé et de la Recheche Medicale (INSERM)

  • Endoscopic View Enhancement using deep learning-based 3D Reconstruction Techniques

In collaboration with the Centre de Recherche en Automatique de Nancy (CRAN)

  • Automatic Categorization of Gastro-Intestinal Inflammations from Endoscopic data using Deep Learning techniques

In collaboration with the Centre de Recherche en Automatique de Nancy (CRAN) and the Laboratoire Imagerie et Vision Artificielle, (ImViA), an Hotpital Ambroise-Paré (PAris)

  • Automatic segmentation and identification of vascular pattern symmetries on cerebral vessels using Deep Learning techniques

In collaboration with the Biomedical Engineering Research Center (CREB, Barcelona) of the

 Universitat Politecnica de Catalunya (Spain) and the Hospital Sant Joan de Deu (Barcelona)

Computer Vision

  • Watch: Wildfire Analysis Through Computer vision Techniques. A collaborative project for early wildfire dentification and ffire widespread forcasting. A collaboration with several Mexican universities and the Universitá di Corsica (France)
Additional information

There are available positions (bachelor, master and PhD) for all these projects via CONACYT and other grants for suitable candidates (contact me for further details). International mobility is strongly encouraged and double diplomas are possible.

I strongly advice any prospect students to show qualifications for deep learning. Showing evidence of previous projects (thesis, articles, GitHub) or qualifications from Coursera (Deep Learning and/or Artificial Intelligence for Medicine) is highly desired.


Tecnológico de Monterrey, Campus Guadalajara
Av. General Ramón Corona #2514.
Col. Nuevo México, CP 45138.
Zapopan, Jal. México
Teléfono 52 (33) 3669 3000