Publications

For a complete and up-to-date list, please visit Google Scholar.

Working Papers

  1. Choo, S., Kim, W. (submitted). Two Mechanisms of Self-Compensation in Autonomous Vehicle Trust: Driver Behaviors and the Hierarchical Trustworthiness Layer.
  2. Choo, S.*, Son, Y.*, Kim, W. (submitted). Which Speech Channel Matters for Which User State? Explainable Acoustic-Linguistic Fusion in Human-Robot Collaboration. (*Co-first authors)
  3. Choo, S., Kim, W. (in preparation). Latent Footprints: Detecting Subliminal Trait Transfer in Hidden Representations.
  4. Jin, H., Choo, S. (in preparation). Quantized PEFT Framework with Bit-First Allocation and Adaptive Rank
  5. Lee, D., Choo, S. (in preparation). Similarity-Aware Directional Alignment Framework for Mitigating Client Drift in Federated Learning.
  6. Koo, B., Choo, S. (in preparation). Fairness-Aware Reasoning Framework for Social Bias Mitigation with RBD.
  7. Jeong, S., Choo, S. (in preparation). Improving Subliminal Learning using Divergence Tokens.

Peer-Reviewed Journal Articles

  1. Lee, E.*, Choo, S.*, Maguire, D., et al. (accepted). Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data. PLOS One. (*Co-first authors)
  2. Ive, J., Bondaronek, P., Yadav, V., Santel, D., Glauser, T., Strawn, J. R., Agasthya, G., Tschida, J., Choo, S., Chandrashekar, M., Kapadia, A. J., & Pestian, J. (accepted). A data-centric approach to detecting and mitigating demographic bias in pediatric mental health text. Communications Medicine.
  3. Choo, S., Park, H., Jung, J., Flores, K., & Nam, C. S. (2024). Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks. Neural Networks, 180, 106665.
  4. Park, D., Park, H., Kim, S., Choo, S., Nam, C. S., Lee, S., & Jung, J. (2023). Spatio-temporal explanation of 3D-EEGNet for brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 4504–4513.
  5. Choo, S., Park, H., Kim, S., Park, D., Jung, J. Y., Lee, S., & Nam, C. S. (2023). Multi-task deep learning for simultaneous emotion and context recognition from EEG. Expert Systems with Applications, 227, 120348.
  6. Choo, S., & Kim, W. (2023). A study on the evaluation of tokenizer performance in natural language processing. Applied Artificial Intelligence, 37(1), 2175112.
  7. Kim, S., Choo, S., Park, D., Park, H., Nam, C. S., Jung, J. Y., & Lee, S. (2023). Designing an XAI interface for BCI experts: A contextual design for explainability. International Journal of Human-Computer Studies, 174, 103009.
  8. Choo, S., & Nam, C. S. (2022). Detecting human trust calibration in automation: A deep learning approach. IEEE Transactions on Human-Machine Systems, 52(4), 774–783.
  9. Pugh, Z., Choo, S., Leshin, J., Lindquist, K., & Nam, C. S. (2022). Emotion depends on context, culture, and their interaction: Evidence from effective connectivity. Social Cognitive and Affective Neuroscience, 17(2), 206–217.
  10. Huang, J., Choo, S., Pugh, Z. H., & Nam, C. S. (2022). Evaluating effective connectivity of trust in human-automation interaction: A dynamic causal modeling approach. Human Factors, 64(6), 1051–1069.
  11. Nam, C. S., Choo, S., Huang, J., & Park, J. (2020). Brain-to-brain neural synchrony during social interactions: A systematic review on hyperscanning studies. Applied Sciences, 10(19), 6669.
  12. Kim, W., Jin, B., Choo, S., Nam, C. S., & Yun, M. H. (2019). Designing of smart chair for monitoring of sitting posture using convolutional neural networks. Data Technologies and Applications, 53(2), 142–155.
  13. Choo, S., & Lee, H. (2018). Learning framework of multimodal Gaussian–Bernoulli RBM. Neurocomputing, 275, 1813–1822.
  14. Choo, S., & Lee, H. (2016). Bayesian network learning framework for data analysis. Journal of Korean Institute of Intelligent Systems, 26(6), 335–342.

Conference Proceedings

  1. Choo, S., Nam C. S. (accepted). Machine learning generalization under small data: Batch size effects in EEG CNN models. Proceedings of the International Conference on Emerging Intelligent Technologies and Systems (EITS).
  2. Choo, S. (2025). Brain-computer interface: From centralized learning to federated learning and beyond. Proceedings of the Korean Institute of Intelligent Systems Conference.
  3. Jang, J., Choi, J., & Choo, S. (2025). Sentiment analysis for recommender system enhancement. Proceedings of the Korean Institute of Intelligent Systems Conference.
  4. Kim, S., Choi, J., & Choo, S. (2025). Data selection optimization for recommendation systems. Proceedings of the Korean Institute of Intelligent Systems Conference.
  5. Choo, S., Shivanna, A., Goether, I., Santel, D., Pestian, J., Glauser, T., & Agasthya, G. (2023). Pediatric anxiety prediction models. Artificial Intelligence Expo, Oak Ridge National Laboratory.
  6. Park, H., Park, D., Kim, S., Choo, S., Nam, C. S., Lee, S., & Jung, J. (2023). CNN explanation using influence functions for EEG analysis. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4436–4440.
  7. Huang, J., Traylor, Z., Choo, S., & Nam, C. S. (2021). Mental workload during multitasking: An EEG study. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65.
  8. Choo, S., Ghasemi, Y., Jeong, H., & Nam, C. S. (2021). Multi-task learning effects on EEG-based cognitive state prediction. Proceedings of the IISE Annual Conference, 334–339.
  9. Choo, S., & Nam, C. S. (2020). CNN-based emotion recognition using functional connectivity EEG features. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64.
  10. Choo, S., & Nam, C. S. (2020). EEG data augmentation via DCGAN for cognitive state recognition. Proceedings of the IISE Annual Conference, 1–6.
  11. Choo, S., Sanders, N., Kim, N., Kim, W., & Nam, C. S. (2019). Detecting human trust calibration in automation: A deep learning approach. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63, 88–90.
  12. Sanders, N., Choo, S., Kim, N., & Nam, C. S. (2019). Neural correlates of trust during automation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63, 83–87.

Book Chapters

  1. Choo, S., & Nam, C. S. (2022). Interactive reinforcement learning and error classification. In Nam, C. S., Jung, J., & Lee, S. (Eds.), Human-Centered Artificial Intelligence: Research and Applications (pp. 127–143). Elsevier.
  2. Choo, S., & Nam, C. S. (2020). Deep learning techniques in neuroergonomics. In Nam, C. S. (Ed.), Neuroergonomics: Principles and Practices (pp. 115–138). Springer.
  3. Sanders, N., Choo, S., & Nam, C. S. (2020). EEG research methodology guide. In Nam, C. S. (Ed.), Neuroergonomics: Principles and Practices (pp. 33–52). Springer.
  4. Nam, C. S., Eskander, E., & Choo, S. (2020). Neural dynamics in human-robot trust. In Nam, C. S., & Lyons, J. (Eds.), Trust in Human-Robot Interaction: Research and Applications (pp. 477–489). Elsevier.