Jorge E. Zambrano Ibujés

Jorge E. Zambrano Ibujés

Ph.D. in Electrical Engineering, Universidad de Chile. Working on Image Processing and AI applied to biometrics and biomedical systems.

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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advising

committees

portfolio

publications

Implementation of a WirelessHART Training System for Upgrading Industrial Automation

Published in IEEE Latin America Transactions, 2016

A hands-on training platform that emulates a WirelessHART industrial network for teaching wireless process-control concepts to engineering students.

Recommended citation: I. Escobar, E. Pruna, O. Chang, A. Navas, J. E. Zambrano and G. Avila, "Implementation of a WirelessHART Training System for Upgrading Industrial," in IEEE Latin America Transactions, vol. 14, no. 6, pp. 2663-2668, June 2016, doi: 10.1109/TLA.2016.7555235
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A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks

Published in IEEE Access, 2020

A single-image 3D iris reconstruction method based on a depth-estimation CNN, trained on synthetic and real iris datasets, that improves recognition performance by 48% over the standard 2D iris code.

Recommended citation: D. P. Benalcazar, J. E. Zambrano, D. Bastias, C. A. Perez and K. W. Bowyer, "A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 98584-98599, 2020, doi: 10.1109/ACCESS.2020.2996563.
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3D Iris Recognition Using Spin Images

Published in 2020 IEEE International Joint Conference on Biometrics (IJCB), 2020

A 3D iris recognition method that applies spin image descriptors to three-dimensional iris surface models, enabling recognition robust to the geometric distortions present in standard 2D approaches.

Recommended citation: D. P. Benalcazar, D. A. Montecino, J. E. Zambrano, C. A. Perez and K. W. Bowyer, "3D Iris Recognition Using Spin Images," in 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1-8, 2020, doi: 10.1109/IJCB48548.2020.9304890.
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Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching

Published in IEEE Access, 2022

A deep-learning approach to iris recognition that uses features from the low-level layers of pretrained CNNs without further training, combined with bit-shifting for robustness against pupil dilation.

Recommended citation: J. E. Zambrano, D. P. Benalcazar, C. A. Perez and K. W. Bowyer, "Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching," in IEEE Access, vol. 10, pp. 41276-41286, 2022, doi: 10.1109/ACCESS.2022.3166910.
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Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector

Published in IEEE Access, 2024

A neuroevolution method that accelerates architecture search by training a classifier on CNN architecture encodings as feature vectors, reducing evaluation cost within genetic algorithm-based search.

Recommended citation: J. I. Pilataxi, J. E. Zambrano, C. A. Perez and K. W. Bowyer, "Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector," in IEEE Access, vol. 12, pp. 11987-12000, 2024, doi: 10.1109/ACCESS.2024.3355804.
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Iris Recognition Using an Enhanced Pre-Trained Backbone Based on Anti-Aliased CNNs

Published in IEEE Access, 2024

An iris recognition method that enhances a pretrained backbone with anti-aliasing techniques to improve shift-invariance, combined with bit-shifting for robustness against pupil dilation.

Recommended citation: J. E. Zambrano, J. I. Pilataxi, C. A. Perez and K. W. Bowyer, "Iris Recognition Using an Enhanced Pre-Trained Backbone Based on Anti-Aliased CNNs," in IEEE Access, vol. 12, pp. 94570-94583, 2024, doi: 10.1109/ACCESS.2024.3425648.
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Deep Learning-Based Differential Diagnosis of Major Depression and Bipolar Disorder Using Microglia-Cellular Sensors and Patient-Derived Small Extracellular Vesicles

Published in Scientific Reports, 2026

A deep learning framework using microglial cells as biosensors and DenseNet121 to differentiate major depressive disorder, bipolar disorder, and healthy controls from fluorescence microscopy images of patient-derived extracellular vesicles.

Recommended citation: J. E. Zambrano, A. Luarte, J. Contreras, J. P. Perez, L. Yantén-Fuentes, M. Prieto, P. Lazcano, U. Wyneken and C. A. Perez, "Deep Learning-Based Differential Diagnosis of Major Depression and Bipolar Disorder Using Microglia-Cellular Sensors and Patient-Derived Small Extracellular Vesicles," in Scientific Reports, vol. 16, no. 1, p. 11679, 2026, doi: 10.1038/s41598-026-47476-9.
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talks

teaching

Signals Analysis (EL3005 / EL3203)

Undergraduate course, Universidad de Chile, Department of Electrical Engineering, 2019

An undergraduate course on discrete and continuous-time signal analysis. Topics include time-domain and frequency-domain analysis, digital and analog filter design, digital and analog modulations, and spatial signal analysis applied to images. Taught continuously from 2019 to 2025.

Computational Intelligence and Robotics Laboratory (EL5206)

Undergraduate course, Universidad de Chile, Department of Electrical Engineering, 2020

A two-part undergraduate laboratory course. As teaching assistant, I contributed to the computational intelligence section, covering pattern recognition techniques and their practical applications. Topics included classical pattern detection algorithms, feature extraction, and deep learning-based object detection using neural networks such as YOLO. Taught from 2020 to 2025.

Introduction to Fuzzy Set Theory and Intelligent Systems (EL7038)

Graduate course, Universidad de Chile, Department of Electrical Engineering, 2020

A graduate course introducing the theoretical foundations of fuzzy set theory and its application to intelligent systems. Topics include fuzzy logic and inference mechanisms, fuzzy control systems, and expert system design. Taught in 2020, 2023, 2024, and 2025.

Introduction to Digital Image Processing (EL7007)

Graduate course, Universidad de Chile, Department of Electrical Engineering, 2021

A graduate course on digital image processing and its applications. Topics include pattern recognition, filter design, convolutional operations, color channel manipulation, and image segmentation. The course also covers applied deep learning using CNNs across domains such as biometrics, biomedical imaging, and mining inspection. Taught from 2021 to 2024.

Thesis Work I (EL7909)

Graduate course, Universidad de Chile, Department of Electrical Engineering, 2024

A graduate course designed to guide Master’s students through the early stages of their thesis work. Topics include the formulation of research objectives and hypotheses, development of the theoretical framework, literature review, and definition of expected results. Progress is monitored through structured evaluations at each stage. Taught in 2024 and 2025.

Thesis Work II (EL7910)

Graduate course, Universidad de Chile, Department of Electrical Engineering, 2025

The second part of the Master’s thesis sequence. Students develop and finalize their research, progressing from preliminary results to the complete thesis document. The course covers scientific writing, results analysis and discussion, conclusions, and preparation of the final thesis submission. Progress is monitored through structured evaluations leading to the final defense. Taught in 2025.

Advanced Programming (EL4203-1)

Undergraduate course, Universidad de Chile, Department of Electrical Engineering, 2026

An advanced undergraduate course covering object-oriented programming, functional paradigms, software design patterns, and AI-assisted development practices using Python.

Programming (CIT1100-CA15)

Undergraduate course, Universidad Diego Portales, Escuela de Ingeniería Informática, 2026

An introductory programming course designed for students in Computer and Telecommunications Engineering and Data Science. The course covers fundamental programming concepts using Python, including variables, control flow, and basic data structures.

Programming (CIT1100-CA20)

Undergraduate course, Universidad Diego Portales, Escuela de Ingeniería Informática, 2026

An introductory programming course designed for students in Computer and Telecommunications Engineering and Data Science. The course covers fundamental programming concepts using Python, including variables, control flow, and basic data structures.

Image Processing (CIT3501_CA01)

Undergraduate course, Universidad Diego Portales, Escuela de Ingeniería Informática, 2026

An advanced undergraduate course on digital image processing, covering the theoretical foundations and practical implementation of techniques for image enhancement, analysis, and computer vision. Topics include spatial and frequency-domain processing, color image analysis, multiresolution representations, filtering, mathematical morphology, segmentation, feature detection and description, Hough transforms, and applications in image understanding using Python.

TICS I (CIT2505)

Undergraduate course, Universidad Diego Portales, Escuela de Ingeniería Informática, 2026

This course aims for students to consolidate the knowledge and skills acquired up to this point in the curriculum through a project that solves a real-world problem, developing the ability to communicate their work effectively through both oral and written presentation.