Teaching Statement
Undergraduate and graduate, courses in electrical engineering and computer science, 2025
Teaching Philosophy and Experience
I believe that teaching goes beyond delivering knowledge—it is about inspiring curiosity, developing problem-solving skills, and empowering students to apply concepts in meaningful, real-world contexts. My goal is to create a dynamic, inclusive learning environment where students are encouraged to explore ideas independently while being supported by clear instruction, engaging content, and constructive feedback.
Previous teaching experience: I have teaching in Colombian Universities, such as Universidad Industrial de Santander and Universidad de Investigación y Desarrollo.
- During my master studies I was assistant of laboratory of numerical analysis using Matlab (2014-2015), my role was to give lab instructions and grade lab experiments.
- During my doctoral studies, I had the opportunity to be an assistant professor on digital image processing in the Department of Electrical Engineering of Universidad Industrial de Santander (Bucaramanga, Colombia). My work was to design exams, grade homework, exams and quizzes and answer questions in office hours. Moreover, I designed new lab project for image processing.
- In the first semester of 2020, I worked as an assistant professor on data structures, fundamentals of programming, technology of information, and communication (TIC) and pursuing research work (2020-2021) at the Department of Computer Science at Universidad de Investigación y Desarrollo, Bucaramanga, Colombia. My work was to design exams, grade homework, exams and quizzes and answer questions in office hours.
Previous student mentoring:
During my postdoctorate in the Pontificia Universidad Católica de Valparaíso (PUCV), I have co-advised the research project of Ph.D. students Alejandro Alvarado, Felipe Guzman, and Bastian Romero.
Teaching plans
While it would be natural for me to teach courses in numerical methods, digital signal processing, digital image processing, computer programming, and computational imaging. I would also enthusiastically teach undergraduate and graduate-level courses in advanced topics on signal processing and digital image processing, for instance, compressive sensing and inverse problem in computational imaging. I would also be specifically interested in developing new courses at the intersection of these fields: compressive sensing and inverse problems in optical engineering and computational imaging, optimization, algorithms and computational complexity, and advanced topics signal processing, digital image processing, and applied machine learning.
Recent advances have shown how novel optical and computational imaging architectures allow us to sample signals in 3D and even higher dimensions. We are closer to the ``holy grail" of capturing all dimensions of light (space, angular position, time, depth, spectrum, and polarization) in a single snapshot. Among those novel architectures for sampling light are single-pixel camera, coded aperture compressive temporal imaging, and coded aperture compressive spectral imaging. I want to develop a course that explicitly brings applied optics challenges to leverage an enormous potential by combining modern artificial intelligence and signal processing in higher dimensions.
Artificial intelligence boosts the quality of reconstruction by exploiting the enormous amount of labeled data. My intention is to offer a course in which students learn to use artificial intelligence, machine learning and deep learning as tools to solve high-dimensional challenges in digital signal and image processing and computational imaging. These tasks range from classification using compressive spectral measurements to reconstruction with pristine quality using convolutional neural networks, generative adversarial networks (GANs), and transformers.
Possible course catalog
The core courses I could teach are:
Undergraduate courses
At the undergraduate level, I am qualified to teach:
* Computer Programming, Object-Oriented Programming, Data Structure Programming,
* Data Science and Machine Learning
* Algorithms and Computational Complexity
* Digital Signal/Image Processing
* Numerical Methods and Analysis
* Linear Algebra
* Probability and Statistics
* Convex Optimization Methods
* Mathematical modeling and simulation
* Discrete Mathematics
* Automata and Formal Languages
* Remote Sensing and Geo-information
* Neural Networks and Deep Learning
Graduate courses
At the graduate level, I can offer specialized courses in:
* Advanced Digital Signal/Image Processing, including Compressive Sensing and Sparse Representation.
* Computational Imaging and Inverse Problems
* Computer Vision and AI.
* Remote Sensing and Earth Observation.
* Data Science, Deep Learning and AI applications.
My teaching philosophy is driven by a passion for research and commitment to student growth. I am to make learning interactive, interdisciplinary, and grounded in real-world applications. Whether through classroom instruction, curriculum development, or research mentorship, I foster an environment where students feel challenged, supported, and inspired.
I am excited to contribute to the academic community at deparment of computer science at the PUCV. I look forward to helping to train the next generation of computer scientists.