YongKyung Oh

YongKyung Oh

Postdoctoral researcher at the UCLA Health - David Geffen School of Medicine.

Research experience

Affiliation

Postdoctoral Researcher at UCLA Health

Research Highlights

  • Excellence in Translational & Methodological AI: Best Paper Award at CHIL 2025 for work on robust AI in medical time series analysis; Spotlight presentation (top 5%) at ICLR 2024 for Neural Differential Equations; publications at multiple top conferences, a comprehensive survey for IJCAI 2025, a tutorial for CIKM 2025, and multiple invited talks.
  • Demonstrated Funding and Leadership Potential: Secured a competitive Postdoctoral Fellowship from the National Research Foundation of Korea (NRF) as a Principal Investigator, demonstrating the ability to lead an independent research program.
  • Consistent Record of Peer-Reviewed Excellence: Recognized as a Best Paper Award Finalist for three consecutive years (2023–2025) by the DAIS division of IISE, showcasing a sustained track record of high-impact research.
  • Proven Track Record of High-Impact Applications: Authored multiple publications in leading journals (e.g., Information Fusion, IEEE T-ASE) and filed several patents on core time series modeling methods and their real-world applications.

Research Interests

  • Physics-Informed Digital Twins for Complex Operations: Integrating physical knowledge with neural differential equations to build high-fidelity models of complex operation systems, with applications in predictive maintenance and automated quality control.
  • Causal Inference in Continuous-Time for Operational Analytics: Discovering causal structures in dynamic, irregular data to enable automated root-cause analysis, with a focus on explainable AI for fault diagnosis and intervention design in industrial processes.
  • Robust Decision-Making Under Uncertainty: Integrating predictive uncertainty from neural stochastic models into operational optimization frameworks and developing distributionally robust methods for operations planning and resource allocation.

Working Papers (Under review)

* Co-first author, † Co-corresponding author

  • Oh, Y., Rahrooh A., Talton, L., & Bui, A. A. T., Identifying a Shift in Researcher Perceptions of Generative AI within the Responsible Conduct of Research Course
  • Oh, Y., Zheng, H., Feng, J., & Bui, A. A. T., Survey-aware Machine Learning: A Methodological Imperative for Reliable, Equitable, and Generalizable Inference from Complex Real-World Samples
  • Oh, Y., Cohen, Z., Akre, S., Zbozinek, T., Craske, M., & Bui, A. A. T., Predicting Depression States Using a Multi-Factor Ensemble Approach
  • Oh, Y., & Bui, A. A. T., Integrating Transfer Entropy into Transformer for Time Series Forecasting
  • Oh, Y., & Bui, A. A. T., Multi-Scale Transformer for Long-Term Time Series Forecasting
  • Oh, Y., & Yeom, J., Leveraging LLM for Sentiment Detection in News Headlines
  • Oh, Y., Koh, G., Shin, K., Moon, J., & Kim, S. Real-Time Risk Assessment of Thyroid Function Abnormality using Irregularly-Sampled Heart Rate Records
  • Oh, Y., Kwak, J., & Kim, S., Semi-Supervised Spatio-Temporal Graph Neural Networks for Detecting Non-Recurrent Traffic Congestion in Intelligent Transportation Systems.

Journal Papers (Published)

  • [J14] 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. [paper]
  • [J13] 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. [paper] [code]
  • [J12] 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. [paper] [code]
  • [J11] 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. [paper] [code]
  • [J10] 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. [paper]
  • [J09] Oh, Y., Kim, S., & Bui, A. A. T. (2025). Deep Interaction Feature Fusion for Robust Human Activity Recognition. In K.-C. Peng, Y. Wang, Z. Li, Z. Chen, J. Yang, S. Suh, & M. Wu (Eds.), Human Activity Recognition and Anomaly Detection (pp. 99–116). Springer Nature. https://doi.org/10.1007/978-981-97-9003-6_7. [paper]
  • [J08] 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. [paper] [code]
  • [J07] 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. [paper] [code]
  • [J06] 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. [paper] [code]
  • [J05] 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. [paper]
  • [J04] 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. [paper] [code]
  • [J03] 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. [paper]
  • [J02] 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. [paper]
  • [J01] 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. [paper]

Conference Papers (In-press or Published)

  • [C26] Oh, Y., Lim, D.†, & Kim, S.† (2026). FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification, The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26), January 2026. (accepted) [paper] [code]
  • [C25] Lee, J.*, Oh, Y.*, Lim, D., Kim, S., & Lim, D. (2026). Continuum Dropout for Neural Differential Equations, The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26), January 2026. (accepted) [paper] [code]
  • [C24] Oh, Y., Lim, D., Kim, S.†, & Bui, A. A. T.† (2025). TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification, The 34th ACM International Conference on Information and Knowledge Management (CIKM 2025), November 2025. [paper] [code]
  • [C23] Oh, Y., Kam, S., Lim, D., & Kim, S. (2025). Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations, The 34th ACM International Conference on Information and Knowledge Management (CIKM 2025), November 2025. [paper]
  • [C22] Oh, Y., Lim, D., & Kim, S. (2025). Neural Differential Equations for Continuous-Time Analysis, The 34th ACM International Conference on Information and Knowledge Management (CIKM 2025), November 2025. Tutorial track. [paper] [slide] [webpage]
  • [C21] Oh, Y. (2025). Continuous-Time Modeling of Complex Operational Systems with Neural Differential Equations, 2025 INFORMS Annual Meeting, October 2025. Job Market Showcase Track.
  • [C20] Oh, Y., Kam, S., Lee, J., Lim, D., Kim, S.†, & Bui, A. A. T.† (2025). Comprehensive Review of Neural Differential Equations for Time Series Analysis. Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 10621–10631. https://doi.org/10.24963/ijcai.2025/1179. [paper] [slide] [poster] [webpage]
  • [C19] Oh, Y., & Bui, A. (2025). Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis. Proceedings of the sixth Conference on Health, Inference, and Learning (Vol. 287, pp. 502–526). PMLR. https://proceedings.mlr.press/v287/oh25a.html. Models & Methods Track - Best Paper Award. [paper] [code] [poster]
  • [C18] Oh, Y., & Bui, A. A. T. (2025), Multi-Scale Transformer for Long-Term Time Series Forecasting, Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo 2025, June 2025. Data Analytics and Information Systems (DAIS) Division - Best Paper Competition Finalist. [slide]
  • [C17] Oh, Y.*, Lim, D.-Y.*, & Kim, S. (2025). DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis. In T. Walsh, J. Shah, & Z. Kolter (Eds.), AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25—March 4, 2025, Philadelphia, PA, USA (pp. 19730–19739). AAAI Press. https://doi.org/10.1609/AAAI.V39I18.34173 [paper] [code] [slide] [poster]
  • [C16] Lin, Y., Zhu, Y., Oh, Y., Zhuang, L., Taira, R., Prosper, A.E., & Hsu, W. (2024). Extracting Quantitative Smoking History from Clinical Narratives Using Zero-Shot Guideline-Based Large Language Model Prompting, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’24), Nov 2024. [paper] [poster]
  • [C15] 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 20th International Conference on Automation Science and Engineering (CASE) 2024, Institute of Electrical and Electronics Engineers (IEEE), Aug 2024. (Journal Track) [paper] [slide]
  • [C14] Oh, Y., Kim, S., & Bui, A. A. T. (2024). Deep Interaction Feature Fusion for Robust Human Activity Recognition, 4th International Workshop on Deep Learning for Human Activity Recognition, The 33rd International Joint Conference on Artificial Intelligence (IJCAI) 2024, Aug 2024. [workshop] [paper] [slide]
  • [C13] Oh, Y., Lim, D., & Kim, S. (2024), Neural CDE-Flow for Irregular Time Series: Integrating Neural Controlled Differential Equations and Neural Flow for Comprehensive Irregularly-sampled Time Series Analysis, Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo 2024, May 2024. Data Analytics and Information Systems (DAIS) Division - Best Paper Competition Finalist. [slide]
  • [C12] Oh, Y.*, Lim, D.*, & Kim, S. (2024). Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data. The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. https://openreview.net/forum?id=4VIgNuQ1pY Spotlight presentation (Notable Top 5%). [paper] [code] [slide] [poster] [news]
  • [C11] Oh, Y., & Kim, S. (2023), Flow-based Neural Differential Equations for Time series Analysis, Conference of Korea Computer Congress 2023 (KCC 2023), June 2023. Artificial Intelligence Division - Best Paper Award. [slide]
  • [C10] Oh, Y., & Kim, S. (2023), Heterogeneous Model Fusion for Enhanced Sensor-Based Human Activity Recognition, Conference of Korea Computer Congress 2023 (KCC 2023), June 2023. Artificial Intelligence Division - Best Oral Presentation Award. [slide]
  • [C09] Oh, Y., & Kim, S. (2023), Neural Stochastic Differential Equations Based on the Ornstein-Uhlenbeck Process, Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo 2023, May 2023. Data Analytics and Information Systems (DAIS) Division - Best Paper Competition Finalist. [slide]
  • [C08] Oh, Y., & Kim, S. (2022), Lifelog data fusion approach for emotion recognition, Conference of Korea Software Congress (KSC) 2022, December 2022. [slide]
  • [C07] Oh, Y., Koh, G., Shin, K., Moon, J., & Kim, S. (2022), Real-Time Risk Assessment of Thyroid Function Abnormality using Irregularly-Sampled Heart Rate Records, The 7th Joint Conference of Korean Artificial Intelligence Association (JKAIA) 2022, November 2022. [poster]
  • [C06] Oh, Y., & Kim, S. (2022), Irregularly sampled time series classification using neural stochastic differential equation, The 7th Joint Conference of Korean Artificial Intelligence Association (JKAIA) 2022, November 2022. [poster]
  • [C05] 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. (Doctoral Consortium). https://doi.org/10.24963/ijcai.2022/835. [paper] [slide]
  • [C04] Oh, Y., & Kim, S. (2021). Multi-channel Convolution Neural Network for Gas Mixture Classification. 2021 International Conference on Data Mining Workshops (ICDMW), 1094–1095. (Ph.D. Forum). https://doi.org/10.1109/ICDMW53433.2021.00143. [paper] [slide]
  • [C03] Oh, Y., & Kim, S. (2021), Multi-channel Convolution Neural Network for Gas Mixture Classification, The 4th Joint Conference of Korean Artificial Intelligence Association (JKAIA) 2021, July 2021. [poster]
  • [C02] Oh, Y., & Kim, S. (2021), Logistics Anomaly Detection with Maritime Big Data: A Bootstrap Approach, Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo 2021, May 2021. Logistics & Supply Chain (LSC) Division - Best Paper Award. [slide]
  • [C01] Oh, Y., & Kim, S. (2019), Exploiting Logistics Anomaly Detection using Maritime Big Data. In IISE Annual Conference. Proceedings (pp. 964-969). Institute of Industrial and Systems Engineers (IISE). Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo, May 2019. [paper] [slide]

Workshop Presentations (Peer-reviewed or Invited)

  • [W11] Oh, Y., Cohen, Z., Akre, S., Zbozinek, T., Craske, M., & Bui, A. A. T. (2025). Interpretable Multi-Factor Framework for Longitudinal Mental Health Monitoring, INFORMS Workshop on Data Science 2025, October 2025. (accepted) [workshop]
  • [W10] Oh, Y., & Bui, A. A. T. (2025). The Compounded Risks of Federated Foundation Model Personalization Under Dataset Shift, International Workshop on Federated Learning with Generative AI, In Conjunction with IJCAI 2025 (FedGenAI-IJCAI’25), The 34th International Joint Conference on Artificial Intelligence (IJCAI 2025), August 2025. (accepted) [workshop] [slide]
  • [W09] Oh, Y., & Bui, A. A. T. (2025). Integrating Transfer Entropy into Transformer for Time Series Forecasting, AAAI’25 Workshop on Artificial Intelligence with Causal Techniques, The 39th Annual AAAI Conference on Artificial Intelligence 2025, February 2025. [workshop] [slide] [poster]
  • [W08] Oh, Y., & Bui, A. A. T. (2025). Multi-view Contrastive Learning for Medical Time Series Domain Adaptation, AI for Medicine and Healthcare, AAAI Bridge Program 2025, The 39th Annual AAAI Conference on Artificial Intelligence 2025, February 2025. [workshop] [slide]
  • [W07] Oh, Y., Kam, S., Lim, D., & Kim, S. (2024). Advancing Irregular Time Series Classification in Astronomy with Neural Stochastic Differential Equations, INFORMS Workshop on Data Science 2024, October 2024. [workshop] [slide]
  • [W06] Oh, Y., Lim, D., Kim, S., & Bui, A. A. T. (2024). Attention-guided Neural Differential Equations Framework for Missingness in Time Series Classification, The 10th Mining and Learning from Time Series (MILETS) Workshop, The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2024, Aug 2024. [workshop] [poster]
  • [W05] Oh, Y., Kam, S., Lim, D., & Kim, S. (2024), Neural Langevin-type Stochastic Differential Equations for Astronomical time series Classification under Irregular Observations, AI4DifferentialEquations in Science Workshop, The Twelfth International Conference on Learning Representations (ICLR) 2024, May 2024. [workshop] [poster]
  • [W04] Oh, Y., Lim, D., & Kim, S. (2024), Invertible Solution of Neural Differential Equations for Analysis of Irregularly-Sampled Time Series. Artificial Intelligence for Time Series (AI4TS) Workshop, The 38th Annual AAAI Conference on Artificial Intelligence 2024, February 2024. [workshop] [poster]
  • [W03] Oh, Y., Kam, S., Lim, D., & Kim, S. (2024), Enhancing Astronomical time series Classification with Neural Stochastic Differential Equations under Irregular Observations, Knowledge Guided ML (KGML) Bridge Program, The 38th Annual AAAI Conference on Artificial Intelligence 2024, February 2024. [workshop] [slide] [poster]
  • [W02] Oh, Y., Lim, D., & Kim, S. (2023), Unifying Neural Controlled Differential Equations and Neural Flow for Irregular Time Series Classification. In The Symbiosis of Deep Learning and Differential Equations (DLDE) III Workshop, The 37th Annual Conference on Neural Information Processing Systems (NeurIPS) 2023, December 2023. [workshop] [poster]
  • [W01] Oh, Y., Kwak, J., & Kim, S. (2022), Time Delay Estimation of Traffic Congestion Based on Statistical Causality, The 3rd Workshop on Data-driven Intelligent Transportation (DIT 2022), The 31st ACM International Conference on Information and Knowledge Management (CIKM) 2022, October 2022. [workshop] [slide]

Talks and Presentations

Link to archive

Industry Cooperation Projects

UCLA Depression Grand Challenge

  • Topic: AI-based solutions to advance depression research and treatment strategies
  • Participation period: Sep. 2023 ~ Present
    • Collaborate with Apple and UCLA Health in pioneering research using mobile technology and wearable devices to identify early indicators of depression and track treatment effectiveness
    • Develop and implement AI-based solutions for several studies, including DMHS (Digital Mental Health Study), and OPTIMA (Operationalizing Digital PhenoTyping In the Measurement of Anhedonia), analyzing digital biomarkers to measure depression symptoms

Human Centered-Carbon Neutral Global Supply Chain Research Center

  • Topic: Proposal writing support for multi-disciplinary research center grant
  • Participation period: Jan. 2023 ~ Aug. 2023
    • Contributed to the core research proposal for a large-scale multi-disciplinary research center focused on carbon neutrality and sustainable supply chain innovation: Safe and Clean Supply Chain (SCSC) center at Pusan National University.
    • Co-designed the data-driven analytics framework for the Data Science Group.

THYROSCOPE Inc.

  • Topic: Classification for Eye disease related to Thyroid illness
  • Participation period: Aug. 2021 ~ Aug. 2023
    • Develop a classification model for eye disease related to thyroid illness, using pictures taken by a DSLR camera
    • Propose a visual symptom recognition model using a vision transformer (TAOD-Net)

THYROSCOPE Inc.

  • Topic: Thyroid hormone prediction with wearable device
  • Participation period: Aug. 2021 ~ Aug. 2023
    • Develop prediction model for thyroid hormone using data collected from wearable device
    • Sequence data modeling with different record frequencies and different modalities
  • Topic: Traffic prediction and congestion modeling using GPS trajectory data
  • Participation period: Nov. 2020 ~ Oct. 2022
    • Develop a traffic prediction model using historic & real-time data
    • Algorithm for predicting traffic congestion from quantitative and qualitative perspectives
    • Causal relationship analysis using traffic incident-oriented speed data

Korea Hydro & Nuclear Power Co., Ltd.

  • Topic: ML-based Diagnosis and Status Analysis for Nuclear Reactor Cores
  • Participation period: Sep. 2020 ~ May 2021
    • Develop a recognition model to diagnose and analyze the status of a reactor core in a nuclear power plant

CyberLogitec

  • Topic: Development of optimization algorithm and solver for large-size problems
  • Participation period: Jun. 2019 ~ Dec. 2019
    • Implement optimization preprocess and solve the algorithm
    • Develop a pipeline for large-size optimization problems using open source solvers including CBC

Ulsan Port Authority

  • Topic: Development of smart port and logistics system
  • Participation period: Jan. 2019 ~ Dec. 2019
    • Analyze automatic identification system (AIS) data for maritime surveillance

Taesung Environmental Research Institute

  • Topic: Analyze sensor data from the gas mixture in manufacturing environment
  • Participation period: Aug. 2018 ~ Jul. 2020
    • Construct research setup and develop pipeline including database setup to deployment
      • Phase 1. Develop linear predictive model (e.g. Generalized Linear Model)
      • Phase 2. Develop non-linear predictive model (e.g. SVM, RF, XGBOOST)
      • Phase 3. Develop multi-layer model

Dissertations

  • Oh, Y. (2023), A Comprehensive Study of Deep Learning for Real-World Multivariate Time Series Classification, Doctoral dissertation paper advised by Prof. Kim, S.
  • Oh, Y. (2017), Discussion of the meaning and components of the fourth industrial revolution, and recommendations for Korea to prepare for the fourth industrial revolution, Master dissertation paper advised by Prof. Gang, K.