Conference proceedings / posters

[144] G. Y. Park, C. Jung, S. Lee, J. C. Ye*, S. W. Lee*
“Self-supervised debiasing using low rank regularization,”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (accepted)

[143] H. Kim and S. W. Lee*
“Robust and efficient grid code transformation for rapid task transfer,”
Computational and Systems Neuroscience (COSYNE), 2024.

[142] J. H. Shin and S. W. Lee*
“Simulation-based behavioral profiling by model-guided task optimization and task-guided data generation,”
Computational and Systems Neuroscience (COSYNE), 2024.

[141] S. Tong, T. Denison, S. W. Lee, and B. Seymour*,
“A novel pain measurement tool by modelling free-operant foraging behaviour in immersive virtual reality,”
Computational and Systems Neuroscience (COSYNE), 2024.

[140] X. Xin and S. W. Lee*,
”Decoding human prediction errors for human-robot value alignment,”
Proceedings of the 12th International Winter Conference on Brain-Computer Interface (IEEE BCI Winter), 2024. (oral presentation)

[139] S. Joo and S. W. Lee*
”Learning Robust Goal Space with Hypothetical Analogy-Making,”
arXiv (preprint), 2024.

[138] G. Y. Park, J. Kim, B. Kim, S. W. Lee* and J. C. Ye*
”Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models,”
Neural Information Processing Systems (NeurIPS), 2023. (acceptance rate = 26%)

[137] M. Song, S. Kang, M. Yang, R. Bruce, D.-S. Choi*, and S. W. Lee*,
“Parvalbumin-Positive Neurons in the Globus Pallidus Externus Modulate Task-Irrelevant Behaviors to Balance Exploration and Exploitation,”
Cognitive Computational Neuroscience (CCN), 2023.

[136] M. A. Yang, K. J. Miller, M. W. Jung, and S. W. Lee*,
“Neural substrates of the flexibly stable learning,”
The 32st Annual Computational Neuroscience Meeting (CNS), 2023.

[135] M. A. Yang, S.-I. Hong, S. Kang, J. Lee, M. Song, D.-S. Choi*, and S. W. Lee*,
“GPe astrocytes selectively represent routine formation,”
The 32st Annual Computational Neuroscience Meeting (CNS), 2023.

[134] G. Y. Park, S. Lee, S. W. Lee*, J. C. Ye*,
“Training debiased subnetworks with contrastive weight pruning,”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. (acceptance rate = 25%)

[133] J. Shin, J. H. Lee, and S. W. Lee*,
“Controlling human cortical and striatal reinforcement learning with meta prediction error,”
Computational and Systems Neuroscience (COSYNE), 2023.

[132] Y. Sung and S. W. Lee*,
“Uncertainty-robust goal embedding in the prefrontal cortex for flexibly stable learning,”
Computational and Systems Neuroscience (COSYNE), 2023.

[131] J. Ryu and S. W. Lee*,
“Generalizable Perceptual Embedding with Noise-Tuning Alignment,”
The 31st Annual Computational Neuroscience Meeting (CNS), 2022.

[130] H. Kim and S. W. Lee*,
“Hippocampal Successor Representation Learning for Zero-shot Navigation,”
Korean Artificial Intelligence Association Summer Conference, 2022.

[129] Y. Sung and S. W. Lee*,
“Uncertainty and goal embeddings in the lateral prefrontal cortex guide flexible and stable reinforcement learning,”
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2022.

[128] S. J. An, B. D. Martino, and S. W. Lee*,
“How human metacognitive exploration improves reinforcement learning in a sparse reward environment,”
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2022.

[127] Y. Cha and S. W. Lee*,
“Information Amplification in Human-AI Interactions via Reinforcement Learning,”
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2022.

[126] D. Kim, J. H. Lee, and S. W. Lee*,
“Exploring essential computations underlying generalizable human reinforcement learning,”
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2022.

[125] G. Lee, M. A. Yang, and S. W. Lee*,
“Memory-guided goal-driven reinforcement learning explains subclinical depression,”
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2022.

[124] S. Joo and S. W. Lee*,
“Learning Robust Task Context with Hypothetical Analogy-Making,”
International Conference on Learning Representations (ICLR) Workshop on Generalizable Policy Learning in the Physical World, 2022.

[123] S. J. An and S. W. Lee*,
“Learning state-space uncertainty, but not value uncertainty, is sufficient for metacognitive exploration,”
Cold Spring Harbor Laboratory meeting: From Neuroscience to Artificially Intelligent Systems (NAISys), 2022.

[122] H. Chi, M. Ha, S. Chi, S. W. Lee, Q. Huang, and K. Ramani,
“InfoGCN: Representation learning for human skeleton-based action recognition,”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (acceptance rate = 25%)

[121] S. J. An, B. D. Martino, and S. W. Lee*,
“Rethinking Tolman's latent learning with metacognitive exploration,”
Computational and Systems Neuroscience (COSYNE), 2022.

[120] J. Shin, J. H. Lee, and S. W. Lee*,
“In silico manipulation of human cortical computation underlying goal-directed learning,”
Computational and Systems Neuroscience (COSYNE), 2022.
*This is an extended version of our poster presentation at the 2021 NeurIPS HMD workshop [ref: 115]. (focusing on neural results)

[119] P. Mahajan, S. W. Lee, B. Seymour,
“Balancing safety and efficiency in human decision-making,”
Computational and Systems Neuroscience (COSYNE), 2022.

[118] M. H. Kim. E. Jo, and S. W. Lee*,
“Goal-driven Atari environment,”
Proceedings of the 10th International Winter Conference on Brain-Computer Interface (IEEE BCI Winter), 2022.

[117] Y. H. Kang and S. W. Lee*,
“Meta-BCI: Perspectives on a role of self-supervised learning in meta brain computer interface,”
Proceedings of the 10th International Winter Conference on Brain-Computer Interface (IEEE BCI Winter), 2022. (oral presentation)

[116] H. Joo, I. Jeong, S. W. Lee*,
”Estimating the level of inference using an order-mimic agent,”
Proceedings of Asian Conference on Pattern Recognition (ACPR), 2021. (link)

[115] J. Shin, J. H. Lee, S. W. Lee*,
”In silico manipulation of human cortical computation underlying goal-directed learning,”
Neural Information Processing Systems (NeurIPS) Workshop on Human and Machine Decisions, 2021. (link)

[114] G. Y. Park and S. W. Lee*,
“Reliably fast adversarial training via latent adversarial perturbation,”
Proceedings of International Conference on Computer Vision (ICCV), 2021. (oral; acceptance rate = 3%) (link).
*The preliminary version of this work was presented at 2021 ICLR Workshop on Security and Safety in Machine Learning Systems [ref: 113].

[113] G. Y. Park and S. W. Lee*,
“Reliably fast adversarial training via latent adversarial perturbation,”
Proceedings of International Conference on Learning Representations (ICLR) Workshop on Security and Safety in Machine Learning Systems, 2021.

[112] G. Y. Park and S. W. Lee*,
“Information-theoretic regularization for multi-source domain adaptation,”
Proceedings of International Conference on Computer Vision (ICCV), 2021. (acceptance rate = 25.9%) (link)

[111] S. J. An, B. D. Martino, and S. W. Lee*,
“Metacognition guides near-optimal exploration of a large state space with sparse rewards,”
Computational and Systems Neuroscience (COSYNE), 2021.

[110] M. Song, S. H. Huh, J. W. Shin, M. W. Jung, and S. W. Lee*,
“Midbrain dopamine activity during reinforcement learning reflects bias-variance tradeoff,”
Computational and Systems Neuroscience (COSYNE), 2021.

[109] Y-J. Cha, S. W. Lee*,
"Human uncertainty inference via deterministic ensemble neural networks,"
Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021. (acceptance rate = 21%) (link)

[108] D. Kim, M. H. Kim, S. W. Lee*.
”Decoding learning strategies from EEG signals provides generalizable features for decoding decision,”
Proceedings of the 9th International Winter Conference on Brain-Computer Interface (IEEE BCI Winter), 2021. (link)

[107] B.-J. Choi, J. Hong, D. Park and S. W. Lee*,
“F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax,”
Proceedings of the Empirical Methods in Natural Language Processing (EMNLP), 2020. (acceptance rate = 22.4%)

[106] J. H. Shin, J. H. Lee and S. W. Lee*,
“Deep Interaction between Reinforcement Learning Algorithms and Human Reinforcement Learning,”
Cold Spring Harbor Laboratory meeting: From Neuroscience to Artificially Intelligent Systems (NAISys), 2020.
(Poster presentation; an extended version of 2020 Korean Artificial Intelligent Association Summer Conference)

[105] J. Ryu and S. W. Lee*,
“Brain-like autoencoder that learns latent covariance structure,”
Cold Spring Harbor Laboratory meeting: From Neuroscience to Artificially Intelligent Systems (NAISys), 2020.

[104] M. Elgaar, J. Park, and S. W. Lee*,
“Multi-speaker and multi-domain emotional voice conversion using factorized hierarchical variational autoencoder,”
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. (link)

[103] M. A. Yang, J. H. Lee, and S. W. Lee*,
“Biological reinforcement learning via predictive spacetime encoding,“
Korean Artificial Intelligent Association Summer Conference, 2020. (in Korean) (Outstanding paper award; link)

[102] J. H. Shin, J. H. Lee, and S. W. Lee*,
”Deep interaction between reinforcement learning algorithms and human reinforcement Learning,”
Korean Artificial Intelligent Association Summer Conference, 2020. (in Korean) (Best paper award; link)

[101] M. Song, S. H. Huh, J. W. Shin, M. W. Jung, and S. W. Lee*,
”Midbrain dopamine activity during reinforcement learning reflects bias-variance tradeoff,”
Korean Artificial Intelligent Association Summer Conference, 2020. (in Korean)

[100] M. Song and S. W. Lee*,
“Dynamic resource allocation during reinforcement learning accounts for ramping and phasic dopamine activity,”
Computational and Systems Neuroscience (COSYNE), 2020.

[99] J. H. Shin, J. H. Lee, S. Tong, S. H. Kim, and S. W. Lee*,
“Designing model-based and model-free reinforcement learning tasks without human guidance”,
Neural Information Processing Systems (NeurIPS) Workshop on Biological and Artificial Reinforcement Learning, 2019.
*The preliminary version of this work was presented at RLDM 2019.

[98] J. H. Shin, J. H. Lee, S. Tong, S. H. Kim, and S. W. Lee*,
“Designing model-based and model-free reinforcement learning tasks without human guidance”,
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019.

[97] D. Kim and S. W. Lee*,
“Behavioral and neural evidence for intrinsic motivation effect on reinforcement learning”,
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019.

[96] D. Kim and S. W. Lee*,
“Deciphering model-based and model-free reinforcement learning strategies and choices from electroencephalography”,
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019.

[95] S. J. An, B. D. Martino, and S. W. Lee*,
“Metacognitive exploration in reinforcement learning”,
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019.

[94] J. Park, K. Han, Y. Jeong, and S. W. Lee*,
“Phonemic-level duration control using attention alignment for natural speech synthesis,”
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019. (link) (Oral presentation)

[93] S. Jung, J. Park, and S. W. Lee*,
“Polyphonic sound event detection using convolutional bidirectional LSTM and synthetic data-based transfer learning,”
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019. (link)

[92] J. Shin, S. Heo, S. A. Lee*, S. W. Lee*,
“Novelty and uncertainty representation in the human brain during flexible learning,”
Korean Society for Cognitive Science, 2019. (in Korean) (Best poster award)

[91] D. Kim, G. Y. Park, J. P. O’Doherty*, and S. W. Lee*,
“Evidence of behavioral and neural interaction between task complexity and state-space uncertainty during reinforcement learning,”
Computational and Systems Neuroscience (COSYNE), 2019.

[90] D. Kim and S. W. Lee*,
”Decoding both intention and learning strategies from EEG signals,”
Proceedings of the 7th International Winter Conference on Brain-Computer Interface (IEEE BCI Winter), 2019. (link)

[89] S. Yoon Do, S. Heo, and S. W. Lee*,
“Effect of depression on prefrontal meta-control of model-based and model-free reinforcement learning,”
KHBM, 2018. (in Korean) (Outstanding poster award)

[88] Y. Kang, S. Heo, and S. W. Lee*,
“Decoupling novelty and uncertainty representation in the human brain during learning and inference,”
KHBM, 2018. (in Korean) (Outstanding poster award)

[87] S. Heo, Y. H. Kim, Y. Do Sung, E. Kang, and S. W. Lee*,
“Impaired Reinforcement Learning Signal Representation in Depression,”
SfN, 2018.

[86] H. Joo, J. Kim, J. Park, J. Shin, J. Jung, J. Jeon, and S. W. Lee*,
“Study on the strategic characteristic of Model-free and Model-based reinforcement learning algorithms in multi-agents environment,”
Proceedings of KIIS Fall Conference, 2018, p. 2. (written in Korean)

[85] J. D. Kralik, J. H. Lee, P. S. Rosenbloom, P. C. Jackson, S. L. Epstein, O. J. Romero, R. Sanz, O. Larue, H. R. Schmidtke, S. W. Lee, and K. Mcgreggor,
“Metacognition for a Common Model of Cognition,”
Proceedings of AAAI 2018 Fall Symposium, 2018. (A full paper was published in Procedia Computer Science. link)

[84] S. J. An and S. W. Lee*,
“Evidence of Human Metacognitive Exploration during Reinforcement Learning,”
The 18th China-Japan-Korea Joint Workshop on Neurobiology and Neuroinformatics, 2018.

[83] C. H. Lee, S. Y. Heo, and S. W. Lee*,
“Deep Neural Experimenter: Hypothesis and Covariate Auto-Verification Paradigm,”
The 18th China-Japan-Korea Joint Workshop on Neurobiology and Neuroinformatics, 2018.

[82] C. Lee and S. W. Lee*,
“Error Backpropagation with Attention Control to Learn Imbalanced Data for Regression,”
Proceedings of IEEE International Conference on Systems; Man; and Cybernetics (IEEE SMC), 2018. (link)

[81] A. Tortay, J. H. Lee, C. H. Lee, and S. W. Lee*,
“Automated Knowledge Base Completion Using Collaborative Filtering and Deep Reinforcement Learning,”
Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), 2018. (link)

[80] J. H. Lee, S. W. Lee, and J. Padget,
“Using Social Reasoning Framework to Guide Normative Behaviour of Intelligent Virtual Agents,”
Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), 2018. (link)

[79] D. Kim and S. W. Lee*
“Model-based BCI : A novel brain-computer interface framework for reading out learning strategies underlying choices,”
Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), 2018. (link)

[78] J. Park and S. W. Lee*,
“Solving the Memory-based-Memoryless Trade-off Problem for EEG Signal Classification,”
Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), 2018. (link)

[77] D. Kim, G. Y. Park, and S. W. Lee*,
“Hierarchical control architecture regulating competition between model-based and context-dependent model-free reinforcement learning strategies,”
Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), 2018, pp. 1–5. (link)

[76] D. Kim and S. W. Lee*,
“Reading out reinforcement learning strategies underlying trial-by-trial choice behavior,”
The Seventh International BCI Meeting: “BCIs: Not Getting Lost in Translation” (BCI meeting 2018), 2018.

[75] S. H. Yi, J. H. Lee, C. H. Lee, J. Kim, S. J. An, and S. W. Lee*,
“A Competitive Path to Build Artificial Football Agents for AI Worldcup,”
Proceedings of IEEE/IEIE International Conference on Consumer Electronics (IEEE/IEIE ICCE) Asia, 2018.

[74] J. Y. Kim and S. W. Lee*,
“Single agent model-based reinforcement learning with state-transition prediction,”
Proceedings of KIIS Spring Conference, 2018, pp. 2–3. (Outstanding poster award)

[73] C. H. Lee, S. Y. Heo, and S. W. Lee*,
“Designing an Experiment without a Human Experimenter,”
Computational and Systems Neuroscience (COSYNE), 2018.

[72] S. J. An and S. W. Lee*,
“Metacognitive exploration in a completely unknown state space,”
Computational and Systems Neuroscience (COSYNE), 2018.

[71] S. Y. Heo and S. W. Lee*,
“Depressive Model-Based and Model-Free Reinforcement Learning,”
Computational and Systems Neuroscience (COSYNE), 2018.

[70] S. Yi, J. Lee, and S. W. Lee*,
“Maximally separating and correlating model-based and model-free reinforcement learning,”
Computational and Systems Neuroscience (COSYNE), 2018.

[69] D. Kim and S. W. Lee*,
“Dynamic encoding of reward and latent task structures in human reinforcement learning,”
Computational and Systems Neuroscience (COSYNE), 2018.

[68] M. R. Song and S. W. Lee*,
“Meta BCI : Hippocampus-Striatum Network Inspired Architecture Towards Flexible BCI,”
Proceedings of the 6th International Winter Conference on Brain-Computer Interface (IEEE BCI Winter), 2018. (link) (Oral presentation)

[67] D. Kim and S. W. Lee*,
“Context-dependent meta-control for reinforcement learning using a Dirichlet process Gaussian mixture model,”
Proceedings of the 6th International Winter Conference on Brain-Computer Interface (IEEE BCI Winter), 2018. (link)

[66] D. Kim, C. Weston, and S. W. Lee*,
“EEG-based classification of learning strategies : model-based and model-free reinforcement learning,”
Proceedings of the 6th International Winter Conference on Brain-Computer Interface (IEEE BCI 2018), 2018. (link)

[65] J. Park, J. Lee, and S. W. Lee*,
“ALPAHCH : A New Approach for LSTM Polynomial Melody Composing based on Finite Chord Progression,”
Proceedings KIIS Spring Conf., 2017. (written in Korean)

[64] S. J. An, J. Y. Kim, and S. W. Lee*,
“Uncertainty-driven state-space learning to resolve exploration-exploitation dilemma,”
Korean Society of Cognitive Science, 2017. (written in Korean)

[63] H. Joo, J. Kim, and S. W. Lee*,
“Model-based reinforcement learning using probabilistic simulation,”
Proceedings of KIIS Fall Conference, 2017, vol. 27. (written in Korean) (Best paper award)

[62] G. Y. Park, D. Kim, and S. W. Lee*,
“Meta reinforcement learning incorporating task complexity,”
Proceedings of KIIS Fall Conference, 2017, vol. 27. (written in Korean)

[61] D. Kim and S. W. Lee*,
“Dirichlet process-based arbitration control of reinforcement learning,”
The 5th International Conference on Robot Intelligence Technology and Applications (ICRITA), 2017.

[60] S. J. An, J. Y. Kim, and S. W. Lee*,
“Metacognitive Reinforcement Learning,”
The 18th International Symposium on Advanced Intelligent Systems, 2017. (Best paper award)

[59] S. W. Lee* and J. P. O’Doherty,
“The role of task complexity during arbitration between model-based and model-free reinforcement learning,”
The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, 2017.

[58] J.-E. Lim, D. Kim, and S. W. Lee*,
“EEG synchrony patterns of autism spectrum disorder,”
Korea society of human brain mapping, 2017. (Outstanding poster award)

[57] S. J. An and S. W. Lee*,
“On the Exploration-Exploitation Dilemma using uncertainty based state space learning algorithm,”
Proceedings KIIS Spring Conf., 2017. (written in Korean) (Outstanding paper award)

[56] J. Kim and S. W. Lee*,
“One-shot learning with Deep Boltzmann machines : an encoding-decoding paradigm,”
Proceedings of KIIS Autumn Conference, 2016. (written in Korean)

[55] J. Park, J. Kim, and S. W. Lee*,
“Multi-agent Cognitive Policy Learning- Reinforcement Learning Through Competition,”
Proceedings of KIIS Autumn Conference, 2016, vol. 26, no. 2. (written in Korean) (Outstanding paper award)

[54] S. W. Lee*,
“Bidirectional transformation between dominant cortical neural activities and phase difference distributions,”
The 25th Annual Computational Neuroscience Meeting (CNS), 2016.

[53] S. W. Lee and Y. S. Kim,
“Insensitive Initialization of LVQ based on IAFC Neural Network,”
Proceedings of KIIS Spring Conference, 2016. (written in Korean) (Outstanding paper award)

[52] S. W. Lee*,
“Space-Time Portraits of Brain Dynamics,”
The 4th IEEE International Winter Conference on Brain-Computer Interface, 2016.

— Before joining KAIST (2003-2015.11) —

[51] S. W. Lee and J. P. O’Doherty, “The effect of state-space complexity on arbitration between model-based and model-free control,” Computational and Systems Neuroscience (COSYNE), 2015.

[50] S. W. Lee, J. P. O’Doherty, and S. Shimojo, “Interplay between learning-rate control and uncertainty minimization during one-shot causal learning,” in Computational and Systems Neuroscience (COSYNE), 2014.

[49] S. W. Lee, J. P. O’Doherty, and S. Shimojo, “Learning the other side of the coin: the neural basis of one-shot learning,” in Tamagawa-Caltech Joint Lecture Course / Reward and Decision-making on Risk and Aversion, 2013.

[48] S. W. Lee, S. Shimojo, and J. P. O’Doherty, “Neural computations underlying arbitration between model-based and model-free learning,” in 20th Joint Symposium on Neural Computation, 2013.

[47] S. W. Lee, J. P. O’Doherty, and S. Shimojo, “Neural computations mediating one-shot learning in the human brain,” in 20th Joint Symposium on Neural Computation, 2013.

[46] S. W. Lee, J. P. O’Doherty, and S. Shimojo, “Neural computations mediating one-shot learning in the human brain,” in 43th annual meeting of the Society for Neuroscience, 2013.

[45] S. W. Lee, S. Shimojo, and J. P. O’Doherty, “Neural correlates of arbitration between model-based and model-free reinforcement learning systems,” in Computational and Systems Neuroscience (COSYNE), 2013.

[44] S. W. Lee, J. Z. Leibo, and T. Poggio, “Peripheral Vision and Crowding in Hierarchical Models of Object Recognition,” in Computational and Systems Neuroscience (COSYNE), 2011.

[43] Z. Bien and S. W. Lee, “Realization of Ageing-friendly Smart Home System with Computational Intelligence,” in Proceedings of the 9th International FLINS Conference on Foundations and Applications of Computational Intelligence, 2010.

[42] S. Bae, S. W. Lee, Y. S. Kim, and Z. Bien, “Fuzzy-State Q-Learning-based Human Behavior Suggestion System in Intelligent Sweet Home,” in Proceedings of the 18th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2009.

[41] Grigorescu, S.M., S. W. Lee, and D. Ristic-Durrant, “Robust Object Recognition in Service Robotics,” Proc. 30th Colloq. Autom., 2009.

[40] S. W. Lee, Y. S. Kim, and Z. Bien, “A Probabilistic Cluster Validity Index for Agglomerative Bayesian Fuzzy Clustering,” in Proceedings of International Conference on Computational Intelligence for Modeling, Control and Automation (CIMCA), 2008.

[39] S. W. Lee, Y. S. Kim, and Z. Bien, “Learning Human Behavior Patterns for Proactive Service System: Agglomerative Fuzzy Clustering- based Fuzzy-state Q-learning,” in Proceedings of International Conference on Computational Intelligence for Modeling, Control and Automation (CIMCA), 2008.

[38] S. M. Grigorescu, S. W. Lee, and D. Ristic-Durrant, “Robust Object Recognition in Service Robotics,” in 30th Colloquium of Automation, 2008.

[37] M. A. Feki, S. W. Lee, Z. Bien, and M. Mokhtari, “Combined Fuzzy State Q-learning Algorithm to predict Context Aware User Activity under uncertainty in Assistive Environment,” in Proceedings of 9th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008.

[36] S. W. Lee, Y. S. Kim, and Z. Bien, “Agglomerative Bayesian Fuzzy Clustering-based Fuzzy-state Q-learning for Life Pattern Prediction.,” in Proceedings of North American Fuzzy Information Processing (NAFIPS), 2008. (accepted, but opted out of the conference proceeding)

[35] M. A. Feki, S. W. Lee, M. Mokhtari, and Z. Bien, “Context Aware Life Pattern Prediction using Fuzzy-State Q-Learning,” in Proceedings of 5th International Conference on Smart homes and health Telematics (ICOST), 2007.

[34] S. W. Lee, Y. S. Kim, and Z. Bien, “Agglomerative Fuzzy Clustering based on Bayesian Interpretation,” in Proceedings of IEEE International Conference on Information Reuse and Integration (IEEE-IRI), 2007.

[33] S. Kim, M. Jeon, S. W. Lee, K.-H. Park, and Z. Bien, “Development of Assistive Software for Disabled and Aged People Based on User Characteristics - Unified User Interface for Special Work Chair,” in Proceedings of 8th International Symposium on advanced Intelligent Systems (ISIS), 2007.

[32] S. W. Lee et al., “Walking Phase Recognition for People with Lower Limb Disability,” in Proceedings of 10th IEEE International Conference on Rehabilitation Robotics (ICORR), 2007.

[31] M. Jeon, J.-H. Do, S. W. Lee, K.-H. Park, and Z. Bien, “Hand Motion Recognition using Fuzzy Decision Tree,” in Proceedings of 8th International Workshop on Human-friendly Welfare Robotic Systems, 2007.

[30] M. Jeon, J.-H. Do, S. W. Lee, K.-H. Park, and Z. Bien, “Multivariate Fuzzy Decision Tree for Hand Motion Recognition,” in Proceedings of 4th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2007.

[29] M. Jeon, J.-H. Do, S. W. Lee, K.-H. Park, and Z. Bien, “A Personalized Hand Gesture Recognition System using Soft Computing Techniques,” in Proceedings of Korea Fuzzy and Intelligent System Autumn Conference, 2007, pp. 127–130. (written in Korean)

[28] S. Kim, M. Jeon, S. W. Lee, K.-H. Park, and Z. Bien, “Development of Assistive Software for Disabled and Aged People Based on User Characteristics - Unified User Interface for Special Work Chair,” in Proceedings of Information and Control Symposium, 2007, pp. 222–224. (written in Korean)

[27] Z. Bien, J.-S. Han, and S. W. Lee, “Feature Subset Selection of Biosignals for Rehabilitation System,” in Proceedings of 28th Colloquium of Automation, 2006.

[26] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “Bayesian Interpretation of Adaptive Fuzzy Neural Network Model,” in Proceedings of IEEE World Congress on Computational Intelligence (WCCI), 2006.

[25] S. W. Lee, D.-J. Kim, Y. S. Kim, J.-W. Jung, and Z. Bien, “A Probabilistic Approach Toward Facial Expression Recognition,” in Proceedings of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2006.

[24] Y.-J. Kwon, D.-J. Kim, S. W. Lee, and Z. Bien, “Sasang Constitution Classifier via Fuzzy Logic,” in Proceedings of JSCI2006, 2006, pp. 68–71. (written in Korean)

[23] D.-J. Kim, S. W. Lee, and Z. Bien, “A Personalized Facial Expression Recognition System using Model Selection/Feature Selection,” in Proceedings of JSCI, 2006, pp. 197–201. (written in Korean)

[22] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “Real-time Facial Expression Recognition System,” in Proceedings of JSCI, 2006, pp. 192–193. (written in Korean)

[21] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “On-line Adaptive Facial Emotional Expression Recognition via Fuzzy Neural Network Model,” in Proceedings of the 14th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2005.

[20] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “Training of Feature Extractor via New Cluster Validity - Application to Adaptive Facial Expression Recognition,” in Proceedings of 9th International Conference on Knowledge-based Intelligence Information & Engineering Systems (KES), 2005.

[19] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “Training of Feature Extractor via New Cluster Validities for Adaptive Facial Expression Recognition,” in Proceedings of 6th International Symposium on Advanced Intelligent Systems (ISIS), 2005. (Outstanding paper award)

[18] D.-J. Kim, S. W. Lee, and Z. Bien, “Facial Emotional Expression Recognition with Soft Computing Techniques,” in Proceedings of 14th IEEE International Workshop on Robot and Human Interactive Communication (IEEE RO- MAN), 2005.

[17] D.-J. Kim, S. W. Lee, and Z. Bien, “Facial Emotional Expression Recognition with Soft Computing Techniques: Real World Applicable Systems,” in Proceedings of the 14th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2005.

[16] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “Adaptive Gabor Wavelet Neural Network for Facial Expression Recognition - Training of Feature Extractor by Novel Feature Separability Criterion,” in Proceedings of 11th World Congress of International Fuzzy Systems Association (IFSA), 2005.

[15] D.-J. Kim, S. W. Lee, and Z. Bien, “Facial Emotional Expression Recognition with Soft Computing Techniques,” in Proceedings of 6th International Symposium on Advanced Intelligent Systems (ISIS), 2005.

[14] Y.-J. Kwon, D.-J. Kim, S. W. Lee, and Z. Bien, “Half-Mirror Interface System for Ubiquitous Environment,” in Proceedings of Human-Computer Interaction Conference (HCI Korea), 2005, pp. 542–546. (written in Korean)

[13] D.-J. Kim, S. W. Lee, and Z. Bien, “A Personalized Facial Expression Recognition System using Model Selection/Feature Selection-Perspective of Performance Comparison,” in Proceedings of Human-Computer Interaction Conference (HCI Korea), 2005, pp. 144–149. (written in Korean)

[12] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “Gabor Wavelet Neural Network-Based Adaptive Facial Expression Recognition System,” in Proceedings of Human-Computer Interaction Conference (HCI Korea), 2005, pp. 108–113. (written in Korean)

[11] J. W. Jung, S. W. Lee, and Z. Bien, “Person Recognition Method using Sequential Walking Footprints via Overlapped Foot Shape and Center-Of-Pressure Trajectory,” in Proceedings of Joint 8th World Multi-Conference on Systems, Cybernetics and Informatics, 2004.

[10] D.-J. Kim, S. W. Lee, and Z. Bien, “A Human-Friendly Human Computer Interaction: Design of Personalized Facial Expression Recognition System,” in Proceedings of Joint 8th World Multi-Conference on Systems, Cybernetics and Informatics, 2004.

[9] S. W. Lee, D.-J. Kim, K.-H. Park, and Z. Bien, “Gabor Wavelet Neural Network-Based Facial Expression Recognition,” in Proceedings of Joint 8th World Multi-Conference on Systems, Cybernetics and Informatics, 2004. (Best paper award)

[8] S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “An Adaptive Facial Expression Recognition System Using Fuzzy Neural Network Model and Q- learning,” in Proceedings of Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2004. (Best paper award)

[7] Z. Bien, D.-J. Kim, and S. W. Lee, “Facial Emotional Expression Recognition with Soft Computing Techniques,” in Proceedings of Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2004.

[6] S. W. Lee, D.-J. Kim, K.-H. Park, and Z. Bien, “Gabor Wavelet Neural Network-Based Facial Expression Recognition,” in Proceedings of the 2nd Joint International Conference on Artificial Intelligence in Engineering and Technology, 2004.

[5] J.-W. Jung, S. W. Lee, and Z. Bien, “Dynamic Footprint-based Person Identification Methods and Its Application to Intelligent Sweet Home,” in Proceedings of Human-Computer Interaction Conference (HCI Korea), 2004. (written in Korean)

[4] S. W. Lee, D.-J. Kim, K.-H. Park, and Z. Bien, “Gabor Wavelet Neural Network-Based Facial Expression Recognition System,” in Proceedings of Human-Computer Interaction Conference (HCI Korea), 2004. (written in Korean)

[3] J. W. Jung, S. W. Lee, and Z. Bien, “Footprint-based Person Identification Method using Mat-type Pressure Sensor,” in Proceedings of International Symposium on Advanced Intelligent Systems (ISIS), 2003.

[2] J.-W. Jung, S. W. Lee, and Z. Bien, “Comparative Analysis of Footprint-Based Person Identification Techniques,” in Proceedings of the 2nd BERC Biometric Workshop, 2003. (written in Korean)

[1] J. W. Jung, Z. Bien, S. W. Lee, and T. Sato, “Dynamic Footprint-based Person Identification using Mat-type Pressure Sensor,” in Proceedings of 25th Annual International Conference of IEEE Engineering in Medicine and Biology Society (IEEE EMBC), 2003.