Publications

International peer-reviewed Conferences and Workshops

[C13] Hassan Shayan, Lakshy Chaudhary, Jonghyeon Ko, Han-Gue Jo, Zero-Shot Perception Modules for Robotic Manipulation: A Comparative Study Across Open-Weights Vision-Language Models. The 15th International Conference on Smart Media & Applications (2026)

[C12] Youngjin Kim, Marco Comuzzi, Moe Wynn and Jonghyeon Ko, A Predictive Process Monitoring Framework Leveraging Enabled Activities via Translucent Event Logs. ASPAI Conference (2026)

[C11] J. Ko*, M. Wynn, M. Comuzzi, F. Maggi, A Reinforcement Learning Framework for Event Log Anomaly Detection and Repair. In International Conference on Process Mining (2025)

[C10] Comuzzi, M., Kim, S., Ko, J., Salamov, M., Cappiello, C., & Pernici, B. (2024). On the Impact of Low-Quality Activity Labels in Predictive Process Monitoring. In International Conference on Process Mining (pp. 201-213).

[C9] J. Ko, F. Maggi, M. Montali, R. Penaloza, and R. pereira (2023) “Plan Recognition as Probabilistic Trace Alignment”, 5th International Conference on Process Mining (ICPM 2023).

[C8] J. Ko,A. Gianola, F. Maggi, M. Montali, and S. Winkler (2023) “Approximating Multi-Perspective Trace Alignment Using Trace Encodings”, 21stInternationalConferenceonBusinessProcessManagement(BPM2023).

[C7] J. Ko*and M. Comuzzi (2022) “Pattern-based Reconstruction of Anomalous Traces in Business Process Event Logs”, In: International Workshop on Computational Intelligence for Process Mining (CI4PM) co-located with the IEEE World Congress on Computational Intelligence (WCCI 2022), p. 18-22.

[C6] J. Ko*and M. Comuzzi (2021) “Business Process Event Log Anomaly Detection based on Statistical Leverage”, Proc. 1th ITalian forum on Business Process Management held in conjunction with BPM 2021.

[C5] J. Ko*and M. Comuzzi (2020) “Online anomaly detection using statistical leverage for streaming business process events”, In: Process Mining Workshops: ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers. Springer Nature, 2021. p. 193.

[C4] J. Ko*, J. Lee, and M. Comuzzi (2020) “AIR-BAGEL: An Interactive Root cause-Based Anomaly Generator for Event Logs”, 2ndInt.Conf.onProcessMining(ICPM)–DemonstrationTrack,pp.35-38

[C3] J. Kim, J. Ko, and S. Lee (2019) “Process discovery and deviation analysis of purchase order handling process”, 9thInternationalBusinessProcessIntelligenceChallenge.

[C2] M. Comuzzi, J. Ko, and S. Lee (2019) “Predicting Outpatient Process Flows to Minimise the Cost of Handling Returning Patients: A Case Study”, Proceedings of the 2nd International Workshop on Process-Oriented Data Science for Healthcare 2019 in conjunction with International Conference on Business Process Management- PODS4H19, pp 557-569.

[C1] J. Ko*and M. Comuzzi (2017) “Fuzzy Analytic Network Process for evaluating ERP post-implementation alternatives”, 2017 IEEE International Conference on Fuzzy Systems - FUZZ-IEEE 2017, pp. 1-6.

International Journals (first/corresponding author with *)

[J17] J. Ko* et al. (Under review) Non-Contact Neuromorphic Emotion Inference via Gaze Interaction, Journal: ACS Nano

[J16] S. Sadeghianasl, D. Fischer, M. Wynn, …, J. Ko. (2026, Accepted) PraeclarusPDQ: A Reference Architecture for Process Data Quality Management, Journal: Journal of Data and Information Quality

[J15] S. Sadeghianasl, M. Wynn, R. Andrews, W. Aalst, J. Ko. (2026) Object-Centric Event-Data Imperfection Patterns, Journal: Information Systems

[J14] M. Comuzzi, S. Kim,J. Ko, C. Cappiello, M. Salamov, B. Pernici. (Under review) On the Impact of Low-Quality Activity Labels on Data-Driven Process Analytics, Journal: International Journal of Data Science and Analytics

[J13] S. Jeong, H. W. Ko, J.-H. Kang, J. Ko* (co-first), …, and S. Mun.(2026) Event-Driven Neuromorphic Gaze Decoding via e-Skin Electrooculography, Journal: ACS Nano

[J12] S. Jeong, J. Ko* (co-first), S. Park, J. Ha, M. S. Chae, L. Kim, S. Mun.(2026) AI-Powered Computer Interface Using Evoked Potentials for XR Biometric Authentication and Individual Neural Profiling, Journal: Biocybernetics and Biomedical Engineering

[J11] J. Ko*, M. Comuzzi, F. Maggi. (2025) Detecting and Repairing Anomaly Patterns in Business Process Event Logs, Journal: Data & Knowledge Engineering

[J10] S. Mun, S. Jeong, J. Ko, J. Kang, S. Bae, Y. Choi. (2025), Advanced AI computing enabled by 2D material-based neuromorphic devices, Journal: npj Unconventional Computing – Nature

[J9] M. Comuzzi, J. Ko, F. Maggi. (2025) A Language to Model and Simulate Data Quality Issues in Process Mining, Journal of Data and Information Quality

[J8] Gianola, A., Ko, J., Maggi, F. M., Montali, M., & Winkler, S. (2025). Approximate conformance checking: Fast computation of multi-perspective, probabilistic alignments. Information Systems, 129, 102510.

[J7] O. Yessenbayev, D. C. D. Nguyen, T. Jeong,K. J.Kang,H. R.Kim, J.Ko, ... & Comuzzi, M. (2024). Combining blockchain and IoT for safe and transparent nuclear waste management: A prototype implementation. Journal of Industrial Information Integration, 39, 100596.

[J6] S. Jeong*,J.Ko*, S. Lee,J.Kang, Y. Kim,S. Y.Park & S. Mun. (2024). Optimizing lane departure warning system towards AI-centered autonomous vehicles. Sensors, 24(8), 2505..

[J5] J. Ko*and M. Comuzzi (2023) “A Systematic Review of Anomaly Detection for Business Process Event Logs”, Business & Information Systems Engineering, 65(4), 441-462.

[J4] J. Ko*and M. Comuzzi (2021) “Keeping our rivers clean: Information-theoretic online anomaly detection for streaming business process events”, Information Systems, 101894.

[J3] J. Ko*and M. Comuzzi (2021) “Detecting anomalies in business process event logs using statistical leverage”, Information Sciences, 549, 53-67.

[J2] B. Tama, M. Comuzzi and J. Ko(2020) “An empirical investigation of different classifiers, encoding and ensemble schemes for next event prediction using business process event logs”, ACM Transactions on Intelligent Systems and Technology, 11(6), 1-34.

[J1] H. Nguyen, S. Lee, J. Kim, J. Koand M. Comuzzi (2019) “Autoencoders for Improving Quality of Process Event Logs”, Expert Systems with Applications, 131, 132-147.