Curriculum Vitae

For a complete and continuously updated list of my publications, please visit my
Google Scholar profile.

You may also download my full CV here:

Download Full CV (PDF)

Education

Doctor of Sciences (Applied Physics)
Benemérita Universidad Autónoma de Puebla (BUAP), 2014

Master of Sciences (Applied Physics)
Benemérita Universidad Autónoma de Puebla (BUAP), 2010

Bachelor’s Degree in Physics
Universidad Pedagógica y Tecnológica de Colombia (UPTC), 2008

Academic and Professional Experience

CEO

Visiting Assistant Professor

Instructor

Research Scholar

Coordinator

Associate Professor

Research Scholar

Technical Skills

Research Focus

My research lies at the intersection of:

• Quantum Computing and Quantum Algorithms
• Quantum Machine Learning
• Machine Learning and Data Science
• Quantum Cryptography and Secure AI
• Hybrid Quantum–Classical Computational Systems

Recent Teaching

Intro to Computation theory

Graduate course, Department of Computer Science, Baylor University, 2025

This course shows the computing foundations (CS 5310). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and computational concepts. On the BU website, you will figure out more information.

Math Toolkit

Undergraduate course, Department of Math and Computer Science, Earlham College, 2025

This course (MATH 195) provides students with a review of the basic mathematical tools they need in computer science field. These concepts are every day in computer scientists’ life. We will focus on discrete mathematics; this field will be helpful for a general audience interested in Computer Science. This course contains theory and discussions: it is a course to think, not to calculate. Find material for this course on official website.


Artificial Intelligence and Machine Learning

Undergraduate course, Department of Math and Computer Science, Earlham College, 2025

This course (CS 365) unveils the core principles of intelligence in machines, from its historical roots to cutting-edge applications. It covers their theoretical underpinnings while providing opportunities to put various techniques into practice. Unravel the fundamental concepts of Neural Networks, Convolutional Neural Networks, and the Bayesian version of ML. You build your own AI through interactive labs, tackling real-world challenges. Prepare to shape the future of intelligent systems. This course contains theory and discussions: it is a course to read, learn about history, it contains topics to think, and to calculate. Find material for this course on official website.


Multivariational Calculus

Undergraduate course, Department of Math and Computer Science, Earlham College, 2025

Our calculus teaching focuses on deep conceptual understanding, not just procedural mastery. We present key ideas through graphs, numbers, algebra, and language, emphasizing how these perspectives connect and reinforce one another. Visual tools, numerical experiments, and clear explanations help make abstract concepts more accessible. We believe technical skills and conceptual insight go hand in hand, each strengthening the other. Calculus isn’t just useful—it represents a profound achievement in human thought. Our goal is to help students appreciate not only its practical applications, but also its elegance and intellectual beauty.

Advanced Algorithms

Graduate course, Department of Computer Science, Baylor University, 2024

Analysis of algorithms performance, time and space comlexity. Graph algorithms, vector and matrix algorithms, adversary arguments, optimal algorithms, adversart arguments, optimal algorithms, parallel algorithms, and current research topoics. Intense converage of NP-completeness with emphasis on recognizing NP-complete problems, proving NP-completeness and creating approximation algorithms (CS 5350).

Statistics Modeling for Data Science

Undergraduate course, Department of Math and Computer Science, Earlham College, 2024

This course, advanced DS 401, provides intensive instruction and participation. The meticulously selected latest edition of the course combines a robust set of resources including archives, exercises, interactive activities and engaging lectures, making it a stimulating and engaging learning experience. The course includes a comprehensive review of basic concepts, particularly the central limit theorem, confidence intervals, regression analysis, model building, and several other related topics. In addition, the course seamlessly combines in-depth theoretical understanding with practical skills through comprehensive Jupyter notebook implementations and hands-on applications in a variety of fields.

Artificial Intelligence and Machine Learning

Undergraduate course, Department of Math and Computer Science, Earlham College, 2024

This course (CS 365) unveils the core principles of intelligence in machines, from its historical roots to cutting-edge applications. It covers their theoretical underpinnings while providing opportunities to put various techniques into practice. Unravel the fundamental concepts of Neural Networks, Convolutional Neural Networks, and the Bayesian version of ML. You build your own AI through interactive labs, tackling real-world challenges. Prepare to shape the future of intelligent systems. This course contains theory and discussions: it is a course to read, learn about history, it contains topics to think, and to calculate. Find material for this course on official website.


Advanced Algorithms

Graduate course, Department of Computer Science, Baylor University, 2023

Analysis of algorithms performance, time and space comlexity. Graph algorithms, vector and matrix algorithms, adversary arguments, optimal algorithms, adversart arguments, optimal algorithms, parallel algorithms, and current research topoics. Intense converage of NP-completeness with emphasis on recognizing NP-complete problems, proving NP-completeness and creating approximation algorithms (CS 5350).

Statistics Modeling for Data Science

Undergraduate course, Department of Math and Computer Science, Earlham College, 2023

This course, advanced DS 401, provides intensive instruction and participation. The meticulously selected latest edition of the course combines a robust set of resources including archives, exercises, interactive activities and engaging lectures, making it a stimulating and engaging learning experience. The course includes a comprehensive review of basic concepts, particularly the central limit theorem, confidence intervals, regression analysis, model building, and several other related topics. In addition, the course seamlessly combines in-depth theoretical understanding with practical skills through comprehensive Jupyter notebook implementations and hands-on applications in a variety of fields.

Physics II

Undergraduate course, Department of Math and Computer Science, Earlham College, 2023

This course (PHYS 230) is a basic course about Electromagnetism, Waves, and Optics, in this context is an introduction to Physics. It considers a review of basic concepts such as Harmonic motion, Electric charge, Electric Field, Electromagnetic waves, Geometric Optics, and more. We will work on physical and mathematical concepts. We will use an Algebra background; therefore, we will go over some theorems or definitions.


Math Toolkit

Undergraduate course, Department of Math and Computer Science, Earlham College, 2023

This course (MATH 195) provides students with a review of the basic mathematical tools they need in computer science field. These concepts are every day in computer scientists’ life. We will focus on discrete mathematics; this field will be helpful for a general audience interested in Computer Science. This course contains theory and discussions: it is a course to think, not to calculate. Find material for this course on official website.


Statistics Modeling for Data Science

Undergraduate course, Department of Math and Computer Science, Earlham College, 2022

This course (DS 401) is a medium level course. It considers a review of basic concepts such as: Central limit theorem, Confidence intervals, Regressions, models, and more. In addition, it provides implementation with Jupyter Notebooks and applications in different areas.

Physics I

Undergraduate course, Department of Math and Computer Science, Earlham College, 2022

This course (PHYS 120) is an introduction to Physics. It considers a review of basic concepts such as: measurements, vectors, Newton’s laws, and more. In addition, it provides a discussion and review of different topics on Physics.


Introduction to Quantum Computing

Graduate course, Department of Computer Science, Baylor University, 2022

This course shows the Quantum Computing foundations (5v93 S1 and S2, 2021 and 2022, respectively). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and physical concepts. On the BU website, you will figure out more information.

Intro to Computation theory

Graduate course, Department of Computer Science, Baylor University, 2022

This course shows the computing foundations (CS 5310). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and computational concepts. On the BU website, you will figure out more information.

Introduction to Quantum Computing

Graduate course, Department of Computer Science, Baylor University, 2021

This course shows the Quantum Computing foundations (5v93 S1 and S2, 2021 and 2022, respectively). For each session, I like implementing new pedagogical and technical knowledge to share mathematical and physical concepts. On the BU website, you will figure out more information.

Tiny Machine Learning

Graduate course, Department of Computer Science, Baylor University, 2021

This course shows Tiny Machine Learning principles. This course is in spanish and you can find on EDX-LatinX.

Mentoring and Supervision

• Angelica Perez, Master’s in Education (Mathematics), Mexico, 2020
• Aaron Rodriguez, Bachelor’s in Applied Mathematics, Mexico, 2022

Honors and Recognitions

• National Researcher Level I (SNI) – National System of Researchers, Mexico (CONACYT)
• APS-Simons Travel and Professional Development Award – American Physical Society, USA

Service and Leadership

• Principal Investigator, Quaker-ECE – Quantum education initiative (2024–present)
• Co-author, Quantum Computing: A Gentle Introduction for Liberal Arts Institutions (2024–present)
• LatinX in AI Mentor, supporting emerging researchers in AI
• Member, American Physical Society (APS)
• Member, Institute of Electrical and Electronics Engineers (IEEE)
• Research Collaborator, BaylorAI – AI, Quantum Computing, and Scientific Computing research group
• Founder, QMexico – Network promoting quantum science in Latin America
• Organizer, Seminar CTIM – interdisciplinary seminar in science and technology
• Co-organizer, Quanta Calaverita outreach and education initiative
• Volunteer, International Conference on Machine Learning (ICML)