◾ PhD in Engineering, Department of Industrial & Management Systems Engineering, Kyung Hee University, 2020
◾ MSc in Engineering, Department of Industrial & Management Systems Engineering, Kyung Hee University, 2014
◾ BS in Computer Science and Systems Engineering, Universidad de San Carlos de Guatemala, 2011

◾ Research Professor, Industrial Artificial Intelligence Laboratory, Kyung Hee University, 2023
◾ Postdocotral Researcher, Industrial Artificial Intelligence Laboratory, Kyung Hee University, 2020 - 2023
◾ Lecturer, Department of Big Data Analytics, Kyung Hee University, 2022 - 2023
◾ Lecturer, Business and Management School, Universidad del Valle de Guatemala, 2021 - 2022
◾ Software Developer, Guatemalan Exporters Association, 2009 - 2011

◾ Explainable Artificial Intelligence, Rule-based machine learning, Ensemble learning
◾ Smart Manufacturing
- Fault detection,
- Quality prediction
◾ Smart Energy
- Batteries State-of-Health estimation
- Renewable energy forecasting

◾ Business Process Management (Fall 2023)
◾ Data Analytics for Electronic Manufacturing (Fall 2023)

◾ Major papers
- Josue Obregon et al.“Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy“, Journal of Energy Storage. (SCIE, IF=8.907, 18.91%(23/109), E&F) .
- Josue Obregon and Jae-Yoon Jung. “RuleCOSI+: Rule extraction for interpreting classification tree ensembles“. Information Fusion. (SCIE, IF=17.564, 0.45%(1/110), CSTM).
- Josue Obregon, Jihoon Hong, Jae-Yoon Jung, “Rule-based Explanations Based on Ensemble Machine Learning for Detecting Sink Mark Defects in the Injection Moulding Process“, Journal of Manufacturing Systems. SCIE, IF=8.633, 0.6%, OR/MS)

◾ Book Chapters
- Josue Obregon, Jae-Yoon Jung, Chapter 4 – Explanation of ensemble models, Human-Centered Artificial Intelligence, Academic Press, 2022, Pages 51-72.

1. Josue Obregon, Yuri Han, Chang Won Ho, Devanadane Mouraliraman, Chang Woo Lee and Jae-Yoon Jung, “Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy“, Journal of Energy Storage Vol. 60, Apr. 2023, (SCIE, IF=8.907, 18.91%(23/109), E&F) .
2. Josue Obregon and Jae-Yoon Jung, “Rule-based visualization of faulty process conditions in the die-casting manufacturing“. Journal of Intelligent Manufacturing (2022), (SCIE, IF=7.136, 20.6% (31/145), CS/AI).
3. Josue Obregon and Jae-Yoon Jung. “RuleCOSI+: Rule extraction for interpreting classification tree ensembles“. Information Fusion, Vol. 89, Jan. 2023, pp. 355-381 (SCIE, IF=17.564, 0.45%(1/110), CSTM).
4. Sun Hur, Jae-Yoon Jung, and Josue Obregon, “Special Issue on Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process“, Processes, 10(4), Mar 2022, 670. (SCIE, IF=3.352, 48.2%, Chem).
5. Josue Obregon, Jihoon Hong, Jae-Yoon Jung*, “Rule-based Explanations Based on Ensemble Machine Learning for Detecting Sink Mark Defects in the Injection Moulding Process“, Journal of Manufacturing Systems, Vol. 60, Jul. 2021, pp. 392-405 (SCIE, IF=8.633, 0.6%, OR/MS)
6. Aekyung Kim, Josue Obregon, and Jae-Yoon Jung*, “PRANAS: A Process Analytics System Using Process Warehouse and Cube“, Applied Sciences, Vol. 10, No. 10, May 2020, 3521. (SCIE, IF=2.217, 45%, AP)
7. Josue Obregon, Aekyung Kim, and Jae-Yoon Jung*, “RuleCOSI: Combination and Simplification of Production Rules from Boosted Decision Trees for Imbalanced Classification“, Expert Systems with Applications, Vol. 126, Jul 2019, pp. 64-82. (SCIE, IF=4.292, 8%, OR/MS)
8. Josue Obregon, Minseok Song, and Jae-Yoon Jung*, InfoFlow: Mining Information Flow Based on User Community in Social Networking Services“, IEEE Access, Vol. 7, Apr 2019, pp. 48024-48036. (SCIE, IF=4.098, 15%, CS/IS)
9. Kwanho Kim, Josue Obregon, and Jae-Yoon Jung*, “Analyzing Information Flow and Context for Facebook Fan Pages“, IEICE Transactions on Information and Systems, Vol. 97-D, No. 4, Apr 2014, pp. 811-814.

1. Jimin Kim, Josue Obregon, Hoonseok Park, and Jae-Yoon Jung, “Photovoltaic power forecasting based on weather forecast and observation using transformer networks”, In Proc. of the International Conference on Innovation Convergence Technology (ICICT2021), Jul 5-6, 2021.(Best Paper Award)
2. Josue Obregon and Jae-Yoon Jung, “RuleLat: A Rule-based Model Visualization Tool for Machine Learning Interpretability”, In Proc. of the 12th International Conference on Internet (ICONI 2020), Dec. 13-16. 2020.
3. Josue Obregon, Aekyung Kim, and Jae-Yoon Jung, “Extracting Simplified Rules from Ensemble Decision Trees”, In Proc. of International Conference on e-Government Enterprise Architecture and Cloud Computing 2018 (eGEAC 2018), Dec 11-12, 2018.
4. Josue Obregon, Aekyung Kim, Hoonseok Park, and Jae-Yoon Jung, “Identifying major defect causes from manufacturing data using decision rules: A case study”, Poster in INFORMS Business Analytics & Operations Research (Analytics 2018), Apr 15-17, 2018.
5. Johannes De Smedt, Seppe Vanden Broucke, Josue Obregon, Aekyung Kim, Jae-Yoon Jung and Jan Vanthienen, “Decision Mining in a Broader Context: A Survey on the Current Landscape and Future Directions”, In Proc. of the 4th Int. Workshop on Decision Mining & Modeling for Business Processes (DeMiMoP’ 2016) in conjunction with BPM 2016, Sep 2016.
6. Josue Obregon and Jae-Yoon Jung, “Merging Production Rules from Ensemble Decision Trees”, In Proc. of the 5th International Conference on Engineering & Technology, Computer, Basic & Applied Sciences (ECBA 2016), June 27-28, 2016.
7. Josue Obregon and Berny Carrera, “Decision Points Analysis in Business Process Using Process Mining”, Process Mining Case Competition of AP-BPM 2015, Jun 2015. (Grand Award)
8. Josue Obregon, Aekyung Kim, and Jae-Yoon Jung, “DTMiner: A Tool for Decision Making Based on Historical Process Data”, In Proc. of the 1st Asia Pacific Conference on Business Process Management (AP-BPM 2013), Aug 2013. (Best Paper Award)
9. Aekyung Kim, Josue Obregon, and Jae-Yoon Jung, “Constructing Decision Trees from Process Logs for Performer Recommendation”, In Proc. of the 1st Int. Workshop on Decision Mining & Modeling for Business Processes (DeMiMoP’13) in conjunction with BPM 2013, Aug 2013.