Assistant Professor
Data Science Major, School of BioMedical Convergence Engineering
Pusan National University
Dr. YongKyung Oh is an Assistant Professor in the Data Science Major, School of BioMedical Convergence Engineering, Pusan National University. His research interests span AI for Science, industrial AI and decision-making, evidence-centered AI, and continuous-time modeling, with an emphasis on scientific discovery and informed decisions. He previously conducted postdoctoral research at the University of California, Los Angeles (UCLA), from 2023 to 2026, and at the Ulsan National Institute of Science and Technology (UNIST). His earlier research experience includes General Motors (GM) and Carnegie Mellon University (CMU). He earned a B.Sc. in Physics, a Master in Technology and Innovation Management, and a Ph.D. in Industrial Engineering, all from UNIST. His recent work includes a sole-authored ICML 2026 position paper on evidence standards. His distinctions include the ICLR 2024 Spotlight, the CHIL 2025 Best Paper Award, and a nomination for the 2025 UCLA Chancellor’s Award for Postdoctoral Research. His research support includes the NRF Postdoctoral Fellowship for Overseas Research as Principal Investigator and an NVIDIA Academic Grant as Lead Scientist.
Pusan National University
Data Science Major · School of BioMedical Convergence Engineering
UCLA Health
David Geffen School of Medicine · Advisor: Alex Bui
Clinical and wearable time series research with the UCLA Depression Grand Challenge.
UNIST
Industry Intelligentization Institute · Advisor: Sungil Kim
Concurrent UNIST–UCLA affiliation, September 2023–November 2024.
UNIST
Data Analytics Lab · Advisor: Sungil Kim
Founding member of the lab; time-series modeling and anomaly detection with industry partners.
Carnegie Mellon University
Language Technologies Institute · Intensive AI Program (High-Potential Individuals Global Training Program)
General Motors
Global Research & Development Center · Manufacturing Systems Research Laboratory
Accenture Korea
Lotte Duty Free Shop New Generation System Integration Project
Institut für Theoretische Physik und Astrophysik
Kiel, Germany · IAESTE program · Advisor: Sebastian Wolf
UNIST
Advisor: Sungil Kim
Dissertation: A Comprehensive Study of Deep Learning for Real-World Multivariate Time Series Classification.
UNIST
Advisor: KwangWook Gang
UNIST
Dual minors in Mechanical Engineering and Advanced Materials Science
Valedictorian · Magna cum laude.
Oh, Y., & Bui, A. (2026). Silent failures in federated personalization of foundation models. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (pp. 13199-13206). Association for Computing Machinery. Blue Sky Ideas Track, Jeju Island, Republic of Korea. https://doi.org/10.1145/3770855.3818661
Oh, Y., Zheng, H. W., Feng, J., & Bui, A. A. T. (2026). Survey-aware machine learning: A guideline for valid population health inference based on scoping review. In Proceedings of the 7th Conference on Health, Inference, and Learning (pp. 198-220). PMLR. Seattle, WA, USA.
Oh, Y., Lim, D.-Y., & Kim, S. (2026). FlowPath: Learning data-driven manifolds with invertible flows for robust irregularly-sampled time series classification. Proceedings of the AAAI Conference on Artificial Intelligence, 40(29), 24594-24603. https://doi.org/10.1609/aaai.v40i29.39643
Lee, J., Oh, Y., Kim, S., & Lim, D.-Y. (2026). Continuum dropout for neural differential equations. Proceedings of the AAAI Conference on Artificial Intelligence, 40(27), 22796-22804. https://doi.org/10.1609/aaai.v40i27.39442
Oh, Y., Kwak, J., & Kim, S. (2026). Predicting non-recurrent congestion impact: A pattern-based approach for speed drop ratio prediction using weighted k-nearest neighbors. Computers & Industrial Engineering, 213, 111769. https://doi.org/10.1016/j.cie.2025.111769
Oh, Y., Lim, D., Kim, S., & Bui, A. A. T. (2025). TANDEM: Temporal attention-guided neural differential equations for missingness in time series classification. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 2232-2242). Association for Computing Machinery. https://doi.org/10.1145/3746252.3760996
Oh, Y., Kam, S., Lim, D., & Kim, S. (2025). Modeling irregular astronomical time series with neural stochastic delay differential equations. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 5068-5073). Association for Computing Machinery. https://doi.org/10.1145/3746252.3760805
Oh, Y., Kam, S., Lee, J., Lim, D.-Y., Kim, S., & Bui, A. A. T. (2025). Comprehensive review of neural differential equations for time series analysis. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 (pp. 10621-10631). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2025/1179
Oh, Y., Kim, S., & Bui, A. A. T. (2025). Deep interaction feature fusion for robust human activity recognition. In Human Activity Recognition and Anomaly Detection (pp. 99-116). Springer Nature. https://doi.org/10.1007/978-981-97-9003-6_7
Oh, Y., Lim, D.-Y., & Kim, S. (2025). DualDynamics: Synergizing implicit and explicit methods for robust irregular time series analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 39(18), 19730-19739. https://doi.org/10.1609/aaai.v39i18.34173
Xue, Y., Zhu, Y., Zhuang, L., Oh, Y., Taira, R., Aberle, D. R., Prosper, A. E., Hsu, W., & Lin, Y. (2025). SmokeBERT: A bidirectional encoder representations from transformers-based model for quantitative smoking history extraction from clinical narratives to improve lung cancer screening. JCO Clinical Cancer Informatics, 9, e2500223. https://doi.org/10.1200/cci-25-00223
Oh, Y., Kim, H., & Kim, S. (2025). TSSI: Time series as screenshot images for multivariate time series classification using convolutional neural networks. Computers & Industrial Engineering, 209, 111393. https://doi.org/10.1016/j.cie.2025.111393
Oh, Y., Koh, G., Kwak, J., Shin, K., Kim, G.-S., Lee, M. J., Choung, H., Kim, N., Moon, J. H., & Kim, S. (2025). TAOD-Net: Automated detection and analysis of thyroid-associated orbitopathy in facial imagery. Computers & Industrial Engineering, 203, 111024. https://doi.org/10.1016/j.cie.2025.111024
Oh, Y., Yoon, K., Park, J., & Kim, S. (2025). Comparative evaluation of VAE-based monitoring statistics for real-time anomaly detection in AIS data. Maritime Policy & Management, 52(4), 609-626. https://doi.org/10.1080/03088839.2024.2388177
Oh, Y., & Kim, S. (2024). Multi-modal lifelog data fusion for improved human activity recognition: A hybrid approach. Information Fusion, 110, 102464. https://doi.org/10.1016/j.inffus.2024.102464
Oh, Y., Lim, C., Lee, J., Kim, S., & Kim, S. (2024). Multichannel convolution neural network for gas mixture classification. Annals of Operations Research, 339(1-2), 261-295. https://doi.org/10.1007/s10479-022-04715-2
Oh, Y., & Kim, S. (2024). Grid-based bayesian bootstrap approach for real-time detection of abnormal vessel behaviors from AIS data in maritime logistics. IEEE Transactions on Automation Science and Engineering, 21(4), 6680-6692. https://doi.org/10.1109/tase.2023.3329041
Oh, Y., Lee, J., & Kim, S. (2023). Sensor drift compensation for gas mixture classification in batch experiments. Quality and Reliability Engineering International, 39(6), 2422-2437. https://doi.org/10.1002/qre.3354
Oh, Y., Kwak, J., & Kim, S. (2023). Time delay estimation of traffic congestion propagation due to accidents based on statistical causality. Electronic Research Archive, 31(2), 691-707. https://doi.org/10.3934/era.2023034
Lee, J., Kwak, J., Oh, Y., & Kim, S. (2023). Quantifying incident impacts and identifying influential features in urban traffic networks. Transportmetrica B: Transport Dynamics, 11(1), 279-300. https://doi.org/10.1080/21680566.2022.2063205
Oh, Y. (2022). Multivariate times series classification using multichannel CNN. In L. D. Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 (pp. 5865-5866). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2022/835
Oh, Y., & Kim, S. (2021). Multi-channel convolution neural network for gas mixture classification. In 2021 International Conference on Data Mining Workshops (ICDMW) (pp. 1094-1095). https://doi.org/10.1109/icdmw53433.2021.00143
Oh, Y., Kim, H., Lee, D., & Kim, S. (2021). Simulation-based anomaly detection in nuclear reactors. Journal of the Korean Institute of Industrial Engineers, 47(2), 130-143. https://doi.org/10.7232/jkiie.2021.47.2.130
Oh, Y., Kim, N., & Kim, S. (2021). Transfer learning based approach for mixture gas classification. Journal of the Korean Institute of Industrial Engineers, 47(2), 144-159. https://doi.org/10.7232/jkiie.2021.47.2.144
NVIDIA
Lead Scientist · In-kind support
National Research Foundation of Korea (NRF)
Principal Investigator
Korea Health Industry Development Institute (KHIDI)
UNIST–UCLA collaborative research
Institute for Information & Communication Technology Planning & Evaluation (IITP)
Training at Carnegie Mellon University (CMU)
UCLA
Center for the Integration of Research, Teaching and Learning
IISE Energy Systems Division
Transform-First Forecasting for County-Level Power Outages
UCLA
UNIST
B.Sc. in Physics
Conference on Health, Inference, and Learning (CHIL)
Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis
Models & Methods Track.
IISE Data Analytics and Information Systems Division
Multi-scale Transformer for Long-term Time Series Forecasting
IISE Data Analytics and Information Systems Division
Integrating Neural Controlled Differential Equations and Neural Flow for Comprehensive Irregularly-sampled Time Series Analysis
Korea Computer Congress (KCC), KIISE
Flow-based Neural Differential Equations for Time Series Analysis
Korea Computer Congress (KCC), KIISE
Heterogeneous Model Fusion for Enhanced Sensor-Based Human Activity Recognition
AI Division.
IISE Data Analytics and Information Systems Division
Neural Stochastic Differential Equations Based on the Ornstein-Uhlenbeck Process
IISE Logistics & Supply Chain Division
Logistics Anomaly Detection with Maritime Big Data: A Bootstrap Approach
Ulsan Development Institute
Strategies for Building a Startup Ecosystem for Port Smartization
Graduate Student Academic Paper Competition.
Dongseo University & Ministry of Maritime Affairs and Fisheries
Suggestion for Smart Port in Industry 4.0
International Conference on Maritime, Academic Paper Competition.
Pusan National University
Instructor
Pusan National University
Instructor
KR 10-2754134 (granted 2025) · US 12,725,326 (granted 2026)
KR 10-2024-0033382 (application pending)
KR 10-2608343 (granted 2023) · JP 7453296 (granted 2024)
Joint applicants: Naver Corp. and UNIST.
KR 10-2604575 (granted 2023) · JP 7453297 (granted 2024)
Joint applicants: Naver Corp. and UNIST.
KR 10-2534357 (granted 2023)
KR 10-2364019 (granted 2022)
KR 10-2250354 (granted 2021)
Scientific Inference Lab
Pusan National University
Yangsan, Korea