Carlos is the author of the book “Machine Learning in Manufacturing: Quality 4.0 and the Zero Defects Vision”. His research work interest lies within the 99% percentile as compared with the cohort of researchers registered in the ResearchGate platform and it has been recognized as one of the most innovative and high impact research topics by the TecReview magazine. He was ranked in the top 3% in TEXATA, the Big Data Analytics World Championships. Carlos was recognized as the SHPE Star of Today by the Society of Hispanic Professional Engineers (SHPE). This award honors an engineer/scientist who has demonstrated outstanding technical excellence resulting in significant accomplishments. It also recognizes dedication, commitment, and selfless efforts to advance Hispanics in STEM careers. Carlos was in the Mexican national team of martial arts. Today he enjoys teaching his colleagues this sport. SHPE: https://www.shpe.org/events/nc2021/programs/star-awards TecReview: https://issuu.com/tecreview/docs/tec\_review-30 ResearchGate profile: https://www.researchgate.net/profile/Carlos\_Escobar31 Google Scholar: https://scholar.google.com/citations?user=3JfYEaUAAAAJ&hl=en
Carlos A. Escobar, PhD
Machine learning research scientist
Research Expertise
About
Publications
Educational experiences with Generation Z
International Journal on Interactive Design and Manufacturing (IJIDeM) / Jul 31, 2020
Hernandez-de-Menendez, M., Escobar Díaz, C. A., & Morales-Menendez, R. (2020). Educational experiences with Generation Z. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(3), 847–859. https://doi.org/10.1007/s12008-020-00674-9
Competencies for Industry 4.0
International Journal on Interactive Design and Manufacturing (IJIDeM) / Nov 02, 2020
Hernandez-de-Menendez, M., Morales-Menendez, R., Escobar, C. A., & McGovern, M. (2020). Competencies for Industry 4.0. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(4), 1511–1524. https://doi.org/10.1007/s12008-020-00716-2
Machine learning techniques for quality control in high conformance manufacturing environment
Advances in Mechanical Engineering / Feb 01, 2018
Escobar, C. A., & Morales-Menendez, R. (2018). Machine learning techniques for quality control in high conformance manufacturing environment. Advances in Mechanical Engineering, 10(2), 168781401875551. https://doi.org/10.1177/1687814018755519
Engineering education for smart 4.0 technology: a review
International Journal on Interactive Design and Manufacturing (IJIDeM) / Jul 29, 2020
Hernandez-de-Menendez, M., Escobar Díaz, C. A., & Morales-Menendez, R. (2020). Engineering education for smart 4.0 technology: a review. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(3), 789–803. https://doi.org/10.1007/s12008-020-00672-x
Technologies for the future of learning: state of the art
International Journal on Interactive Design and Manufacturing (IJIDeM) / Nov 15, 2019
Hernandez-de-Menendez, M., Escobar Díaz, C., & Morales-Menendez, R. (2019). Technologies for the future of learning: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(2), 683–695. https://doi.org/10.1007/s12008-019-00640-0
Quality 4.0: a review of big data challenges in manufacturing
Journal of Intelligent Manufacturing / Apr 11, 2021
Escobar, C. A., McGovern, M. E., & Morales-Menendez, R. (2021). Quality 4.0: a review of big data challenges in manufacturing. Journal of Intelligent Manufacturing, 32(8), 2319–2334. https://doi.org/10.1007/s10845-021-01765-4
Big Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy
Journal of Manufacturing Science and Engineering / Aug 24, 2017
Abell, J. A., Chakraborty, D., Escobar, C. A., Im, K. H., Wegner, D. M., & Wincek, M. A. (2017). Big Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy. Journal of Manufacturing Science and Engineering, 139(10). https://doi.org/10.1115/1.4036833
Learning analytics: state of the art
International Journal on Interactive Design and Manufacturing (IJIDeM) / Jun 18, 2022
Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning analytics: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(3), 1209–1230. https://doi.org/10.1007/s12008-022-00930-0
Biometric applications in education
International Journal on Interactive Design and Manufacturing (IJIDeM) / Jul 28, 2021
Hernandez-de-Menendez, M., Morales-Menendez, R., Escobar, C. A., & Arinez, J. (2021). Biometric applications in education. International Journal on Interactive Design and Manufacturing (IJIDeM), 15(2–3), 365–380. https://doi.org/10.1007/s12008-021-00760-6
Machine Learning and Pattern Recognition Techniques for Information Extraction to Improve Production Control and Design Decisions
Advances in Data Mining. Applications and Theoretical Aspects / Jan 01, 2017
Escobar, C. A., & Morales-Menendez, R. (2017). Machine Learning and Pattern Recognition Techniques for Information Extraction to Improve Production Control and Design Decisions. In Lecture Notes in Computer Science (pp. 286–300). Springer International Publishing. https://doi.org/10.1007/978-3-319-62701-4_23
Process-Monitoring-for-Quality — Big Models
Procedia Manufacturing / Jan 01, 2018
Escobar, C. A., Abell, J. A., Hernández-de-Menéndez, M., & Morales-Menendez, R. (2018). Process-Monitoring-for-Quality — Big Models. Procedia Manufacturing, 26, 1167–1179. https://doi.org/10.1016/j.promfg.2018.07.153
Quality 4.0 — Green, Black and Master Black Belt Curricula
Procedia Manufacturing / Jan 01, 2021
Escobar, C. A., Chakraborty, D., McGovern, M., Macias, D., & Morales-Menendez, R. (2021). Quality 4.0 — Green, Black and Master Black Belt Curricula. Procedia Manufacturing, 53, 748–759. https://doi.org/10.1016/j.promfg.2021.06.085
Process-Monitoring-for-Quality—Applications
Manufacturing Letters / Apr 01, 2018
Escobar, C. A., Wincek, M. A., Chakraborty, D., & Morales-Menendez, R. (2018). Process-Monitoring-for-Quality—Applications. Manufacturing Letters, 16, 14–17. https://doi.org/10.1016/j.mfglet.2018.02.004
Quality 4.0 – an evolution of Six Sigma DMAIC
International Journal of Lean Six Sigma / May 03, 2022
Escobar, C. A., Macias, D., McGovern, M., Hernandez-de-Menendez, M., & Morales-Menendez, R. (2022). Quality 4.0 – an evolution of Six Sigma DMAIC. International Journal of Lean Six Sigma, 13(6), 1200–1238. https://doi.org/10.1108/ijlss-05-2021-0091
Process-Monitoring-for-Quality — A Model Selection Criterion for l - Regularized Logistic Regression
Procedia Manufacturing / Jan 01, 2019
Escobar, C. A., & Morales-Menendez, R. (2019). Process-Monitoring-for-Quality — A Model Selection Criterion for l - Regularized Logistic Regression. Procedia Manufacturing, 34, 832–839. https://doi.org/10.1016/j.promfg.2019.06.166
Process-Monitoring-for-Quality — A Model Selection Criterion for Support Vector Machine
Procedia Manufacturing / Jan 01, 2019
Escobar, C. A., & Morales-Menendez, R. (2019). Process-Monitoring-for-Quality — A Model Selection Criterion for Support Vector Machine. Procedia Manufacturing, 34, 1010–1017. https://doi.org/10.1016/j.promfg.2019.06.094
Process-monitoring-for-quality — A model selection criterion
Manufacturing Letters / Jan 01, 2018
Escobar, C. A., & Morales-Menendez, R. (2018). Process-monitoring-for-quality — A model selection criterion. Manufacturing Letters, 15, 55–58. https://doi.org/10.1016/j.mfglet.2018.01.001
The decay of Six Sigma and the rise of Quality 4.0 in manufacturing innovation
Quality Engineering / May 18, 2023
Escobar, C. A., Macias-Arregoyta, D., & Morales-Menendez, R. (2023). The decay of Six Sigma and the rise of Quality 4.0 in manufacturing innovation. Quality Engineering, 1–20. https://doi.org/10.1080/08982112.2023.2206679
Process-monitoring-for-quality — A machine learning-based modeling for rare event detection
Array / Sep 01, 2020
Escobar, C. A., Morales-Menendez, R., & Macias, D. (2020). Process-monitoring-for-quality — A machine learning-based modeling for rare event detection. Array, 7, 100034. https://doi.org/10.1016/j.array.2020.100034
Process-Monitoring-for-Quality - A Step Forward in the Zero Defects Vision
SAE Technical Paper Series / Apr 14, 2020
Escobar, C., Arinez, J., & Morales-Menendez, R. (2020, April 14). Process-Monitoring-for-Quality - A Step Forward in the Zero Defects Vision. SAE Technical Paper Series. https://doi.org/10.4271/2020-01-1302
Process monitoring for quality — A multiple classifier system for highly unbalanced data
Heliyon / Oct 01, 2021
Escobar, C. A., Macias, D., & Morales-Menendez, R. (2021). Process monitoring for quality — A multiple classifier system for highly unbalanced data. Heliyon, 7(10), e08123. https://doi.org/10.1016/j.heliyon.2021.e08123
Process-Monitoring-for-Quality—A Model Selection Criterion for Genetic Programming
Lecture Notes in Computer Science / Jan 01, 2019
Escobar, C. A., Wegner, D. M., Gaur, A., & Morales-Menendez, R. (2019). Process-Monitoring-for-Quality—A Model Selection Criterion for Genetic Programming. In Evolutionary Multi-Criterion Optimization (pp. 151–164). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_13
Prognosis patients with COVID-19 using deep learning
BMC Medical Informatics and Decision Making / Mar 26, 2022
Guadiana-Alvarez, J. L., Hussain, F., Morales-Menendez, R., Rojas-Flores, E., García-Zendejas, A., Escobar, C. A., Ramírez-Mendoza, R. A., & Wang, J. (2022). Prognosis patients with COVID-19 using deep learning. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-022-01820-x
Interpreting learning models in manufacturing processes: Towards explainable AI methods to improve trust in classifier predictions
Journal of Industrial Information Integration / Jun 01, 2023
Goldman, C. V., Baltaxe, M., Chakraborty, D., Arinez, J., & Diaz, C. E. (2023). Interpreting learning models in manufacturing processes: Towards explainable AI methods to improve trust in classifier predictions. Journal of Industrial Information Integration, 33, 100439. https://doi.org/10.1016/j.jii.2023.100439
Process-Monitoring-for- Quality — A Model Selection Criterion for Shallow Neural Networks
Annual Conference of the PHM Society / Sep 22, 2019
Escobar, C. A., & Morales-Menendez, R. (2019). Process-Monitoring-for- Quality — A Model Selection Criterion for Shallow Neural Networks. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.816
Learning with Missing Data
2020 IEEE International Conference on Big Data (Big Data) / Dec 10, 2020
Escobar, C. A., Arinez, J., Macias, D., & Morales-Menendez, R. (2020, December 10). Learning with Missing Data. 2020 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata50022.2020.9377785
Process-monitoring-for-quality—A robust model selection criterion for the logistic regression algorithm
Manufacturing Letters / Oct 01, 2019
Escobar, C. A., & Morales-Menendez, R. (2019). Process-monitoring-for-quality—A robust model selection criterion for the logistic regression algorithm. Manufacturing Letters, 22, 6–10. https://doi.org/10.1016/j.mfglet.2019.09.001
Discrete Event Simulation
Simulation‐Based Lean Six‐Sigma and Design for Six‐Sigma / Feb 06, 2006
Discrete Event Simulation. (2006, February 6). Simulation‐Based Lean Six‐Sigma and Design for Six‐Sigma; Wiley; Portico. https://doi.org/10.1002/9780470047729.ch5
Augmentation of Body-in-White Dimensional Quality Systems through Artificial Intelligence
2021 IEEE International Conference on Big Data (Big Data) / Dec 15, 2021
Escobar, C. A., Chakraborty, D., Arinez, J., & Morales-Menendez, R. (2021, December 15). Augmentation of Body-in-White Dimensional Quality Systems through Artificial Intelligence. 2021 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata52589.2021.9671610
Process monitoring for quality–a feature selection method for highly unbalanced binary data
International Journal on Interactive Design and Manufacturing (IJIDeM) / Feb 17, 2022
Escobar Diaz, C. A., Arinez, J., Macías Arregoyta, D., & Morales-Menendez, R. (2022). Process monitoring for quality–a feature selection method for highly unbalanced binary data. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(2), 557–572. https://doi.org/10.1007/s12008-021-00817-6
Correction to: Process monitoring for quality - a feature selection method for highly unbalanced binary data
International Journal on Interactive Design and Manufacturing (IJIDeM) / Apr 07, 2022
Escobar Diaz, C. A., Arinez, J., Macías Arregoyta, D., & Morales-Menendez, R. (2022). Correction to: Process monitoring for quality - a feature selection method for highly unbalanced binary data. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(2), 573–573. https://doi.org/10.1007/s12008-022-00871-8
Education
Harvard University
Masters, Management / August, 2024 (anticipated)
Monterrey Institute of Technology and Higher Education
PhD, Engineering Sciences / 2019
New Mexico State University
Masters
Monterrey Institute of Technology and Higher Education
Masters, Quality and Systems Engineering / 2005
Experience
Harvard
Amazon
General Motors
Links & Social Media
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