Javier Orduz

Javier Orduz

Computational Physicist | Quantum & Machine Learning Researcher | Bridging Theory, Data Science, and Emerging Technologies.

Sitemap

A list of all the posts and pages found on my site.

Pages

Dr. Javier Orduz

Computational physicist and researcher in quantum computing, machine learning, and data science.

Posts

events

2026 Iceqt

Published:

title: “International Conference on Quantum and Emergent Technologies”
collection: events
layout: single
permalink: /events/2026/iceqt
date: 2026-07-20
venue: “Las Vegas, NV”
location: “USA”
type: “Archived”
excerpt: “An international conference on Quantum and Emergent Technologies.”
author_profile: true
internal_page: “/events/2026/iceqt”

external_url: “https://lab.rivas.ai/iceqt/”

cta_label: “Visit Main Website”
image: “/images/ICEQT2026.png”
tags: [“Research”, “Collaboration”, “Science”,”Proposals”,”Scientific writting”]

experience

Research Scholar

Published:

  • March 2016: Postdoc
    • DGAPA-UNAM
    • Topics:
      • Education
      • Physics
      • Science Education
      • Applications and theoretical

Associate Professor

Published:

  • February 2017-2020: Associate Professor
    • UNAM
    • Topics:
      • Mathematics
      • Computation
      • Programming
      • Science education
      • Applications and theoretical

Coordinator

Published:

  • July 2020-: Founder and Coodinator
    • QMexico
    • Activities:
      • Research
      • Education
      • Problem-solution

Research Scholar

Published:

  • November 2020: Research Scholar
    • Baylor University
    • Topics:
      • Quantum Computing
      • Quantum Machine Learning
      • Applications and theoretical

Instructor

Published:

  • January 2021-2025: Instructor (online)

Visiting Assistant Professor

Published:

  • July 2022-2025: Instructor
    • Earlham College
    • Courses:
      • Multivariate Calculus (CS 350)
      • Artificial Intelligence and Machine Learning (CS 365)
      • Elementary Statistics (Math 120)
      • Statistics Modeling for Data Science (DS 401)
      • Math Toolkit (Math 195)
      • Programming and Problem Solving (CS 128)
      • Topics in Cybersecurity: Cryptography (CS 295)
      • Physics I (Phys 120)
      • Physics II (Phys 230)

CEO

Published:

  • July 2025-: CEO
    • qaldas
    • Activities:
      • Consulting
      • Research
      • Education
      • Software & Hardware

portfolio

publications

One-loop decays A0→ZZ,Zγ,γγ within the 2HDM and its search at the LHC

Published in Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields, 2014

DOI: 10.1103/physrevd.90.095019

Recommended citation: J. Lorenzo Díaz-Cruz, C. G. Honorato, M. A. Pérez, Javier Orduz. "One-loop decays A0→ZZ,Zγ,γγ within the 2HDM and its search at the LHC." Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields, 2014. DOI: 10.1103/physrevd.90.095019. https://doi.org/10.1103/physrevd.90.095019

Técnicas en informática educativa (TIE): LaTeX y Python (herramientas para la enseñanza de las ciencias)

Published in Revista Mexicana de Bachillerato a Distancia, 2016

DOI: 10.22201/cuaed.20074751e.2016.15.57383

Recommended citation: Javier Orduz. "Técnicas en informática educativa (TIE): LaTeX y Python (herramientas para la enseñanza de las ciencias)." Revista Mexicana de Bachillerato a Distancia, 2016. DOI: 10.22201/cuaed.20074751e.2016.15.57383. https://doi.org/10.22201/cuaed.20074751e.2016.15.57383

Evaluating Accuracy and Adversarial Robustness of Quanvolutional Neural Networks

Published in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021

DOI: 10.1109/csci54926.2021.00097

Recommended citation: Korn Sooksatra, Pablo Rivas, Javier Orduz. "Evaluating Accuracy and Adversarial Robustness of Quanvolutional Neural Networks." 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021. DOI: 10.1109/csci54926.2021.00097. https://doi.org/10.1109/csci54926.2021.00097

Human Activity Classification Using Basic Machine Learning Models

Published in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021

DOI: 10.1109/csci54926.2021.00093

Recommended citation: Bikram Khanal, Pablo Rivas, Javier Orduz. "Human Activity Classification Using Basic Machine Learning Models." 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021. DOI: 10.1109/csci54926.2021.00093. https://doi.org/10.1109/csci54926.2021.00093

Hybrid Quantum Variational Autoencoders for Representation Learning

Published in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021

DOI: 10.1109/csci54926.2021.00085

Recommended citation: Pablo Rivas, Zhao Liang, Javier Orduz. "Hybrid Quantum Variational Autoencoders for Representation Learning." 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021. DOI: 10.1109/csci54926.2021.00085. https://doi.org/10.1109/csci54926.2021.00085

Quantum Machine Learning: A Case Study of Grover’s Algorithm

Published in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021

DOI: 10.1109/csci54926.2021.00088

Recommended citation: Bikram Khanal, Pablo Rivas, Javier Orduz, Alibek Zhakubayev. "Quantum Machine Learning: A Case Study of Grover’s Algorithm." 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021. DOI: 10.1109/csci54926.2021.00088. https://doi.org/10.1109/csci54926.2021.00088

A review of Earth Artificial Intelligence

Published in Computers & Geosciences, 2022

DOI: 10.1016/j.cageo.2022.105034

Recommended citation: Ziheng Sun, L. Sandoval, Robert Crystal‐Ornelas, S. Mostafa Mousavi, Jinbo Wang, Cindy Lin, Nicoleta Cristea, Daniel Tong, Wendy Hawley Carande, Xiaogang Ma, Yuhan Rao, James A. Bednar, Amanda Tan, Jianwu Wang, Sanjay Purushotham, Thomas E. Gill, Julien Chastang, Daniel L. Howard, Benjamin Holt, Chandana Gangodagamage, Peisheng Zhao, Pablo Rivas, Zachary Chester, Javier Orduz, Aji John. "A review of Earth Artificial Intelligence." Computers & Geosciences, 2022. DOI: 10.1016/j.cageo.2022.105034. https://doi.org/10.1016/j.cageo.2022.105034

AI ethics for earth sciences

Published in Elsevier eBooks, 2023

DOI: 10.1016/b978-0-323-91737-7.00007-4

Recommended citation: Pablo Rivas, Christopher C. Thompson, Brenda Tafur, Bikram Khanal, Olawale Ayoade, Tonni Das Jui, Korn Sooksatra, Javier Orduz, Gissella Bejarano. "AI ethics for earth sciences." Elsevier eBooks, 2023. DOI: 10.1016/b978-0-323-91737-7.00007-4. https://doi.org/10.1016/b978-0-323-91737-7.00007-4

Contributors

Published in Elsevier eBooks, 2023

DOI: 10.1016/b978-0-323-91737-7.09991-6

Recommended citation: Sahara Ali, Ahmed Alnuaim, Olawale Ayoade, Jayme Garcia Arnal Barbedo, Colin M. Beier, Gissella Bejarano, Andrew Bennett, Guido Cervone, Nicoleta Cristea, Annie Didier, Geetha Satya Mounika Ganji, Edwin Goh, Weiming Hu, Yiyi Huang, Didarul Islam, Aji John, Lucas K. Johnson, Tonni Das Jui, Amruta Kale, Bikram Khanal, Wai Hang Chow Lin, Xiaogang Ma, M. J. Mahoney, Arif Masrur, Javier Orduz, Nurul Rafi, Pablo Rivas, Korn Sooksatra, Ziheng Sun, Brenda Tafur, Christopher C. Thompson, Jianwu Wang, Jinbo Wang, Kehan Yang, George S. Young, Manzhu Yu. "Contributors." Elsevier eBooks, 2023. DOI: 10.1016/b978-0-323-91737-7.09991-6. https://doi.org/10.1016/b978-0-323-91737-7.09991-6

talks

teaching

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.

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.

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, 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.

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.

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.

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.

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.

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.

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).

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.

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.

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).

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.

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.

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.

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.