Laboratory for Brain and Machine Intelligence

Laboratory for Brain and Machine Intelligence @ KAIST

Laboratory for brain and machine intelligence, KAIST


Working papers

  1. D. Kim, G. Y. Park, J. P. O’Doherty*, and S. W. Lee*, “Task complexity interacts with state-space uncertainty in the arbitration process between model-based and model-free reinforcement-learning at both behavioral and neural levels,” bioRxiv. (https://doi.org/10.1101/393983) (under review)

  2. S. Heo and S. W. Lee*, “Effects of depression on prefrontal striatal goal directed and habitual control,” bioRxiv. (https://doi.org/10.1101/381152) (under review)

  3. J. Lee, J. D. Kralik, Y. Cha, S. Heo, and S. W. Lee*, “Decoupling between causal understanding and awareness during learning and inference,” bioRxiv. (http://dx.doi.org/10.1101/391938) (under review)

  4. M. R. Song and S. W. Lee*, “How do dopamine neurons resolve a tradeoff between performance and energy?,” bioRxiv. (https://doi.org/10.1101/381103) (under review)

  5. D. Kim and S. W. Lee*, “Dynamic encoding of task structure changes in model-based and model-free reinforcement learning”. (under review)

  6. K. Seo, C. Lee, H. Choi, and S. W. Lee*, “Boosting neural network training with hypothetical sample synthesis”. (under review)

  7. S. Heo, C. H. Lee and S. W. Lee*, “Encoding and decoding hypotheses for highly complex experimental design”. (under review)

  8. S. J. An, B. Martino, S. W. Lee*, “Metacognitive exploration in reinforcement learning”. (under review)

  9. J. H. Lee and S. W. Lee*, “Optimal control of human inference processes”.


Journal publications

(*: corresponding authors, +: co-first authors)
Google scholar citations

  1. Weissengruber S.+, Lee S. W.+, John P. O'Doherty, Christian C. Ruff, Neurostimulation reveals context-dependent arbitration between model-based and model-free learning, Cerebral Cortex (2019).

  2. Lee S. W. * and Seymour B.*, Decision-making in brains and robots - the case for an interdisciplinary approach, Current Opinion in Beh. Sci. 26, 137-145 (2019).

  3. Lee, J. H.+, Seymour, B.+* Leibo, J. S., An, J., Lee, S. W.*, Towards high-performance, memory-efficient, and fast reinforcement learning: lessons from decision neuroscience, Science Robotics. 4, eaav2975 (2019).

  4. Wang, O., Lee, S.W., O’Doherty, J., Seymour, B., and Wako, Y. Model-based and model-free pain avoidance learning. Brain Neurosci. Adv. 2 (2018).

  5. Joo, H., Kim, J., and Lee, S.W.* Model-based reinforcement learning using probabilistic simulation. J. Korean Inst. Intell. Syst. 27, 1–5 (2018). (written in Korean)

  6. Lee, S. W.* Reinforcement learning: from algorithms to neuroscience. Commun. KIISE, 36, 8–16 (2018). (written in Korean)

  7. Choung, O., Lee, S. W.* & Jeong, Y.* Exploring feature dimensions to learn a new policy in an uninformed reinforcement learning task. Scientific Reports 7, 17676 (2017).

  8. Lee, S. W.*, Yi, T., Han, J.-S., Jung, J.-W., and Bien, Z. Design of a gait phase recognition system that can cope with EMG electrode location variation. IEEE Transactions on Automation Science and Engineering 14(3) (2017).

  9. An, S. J. & Lee, S. W.* A study on the exploration-exploitation dilemma using an uncertainty-driven state space learning algorithm. J. Korean Inst. Intell. Syst. 27, (2017). (written in Korean)

    == Before joining KAIST  ==

  10. Lee, S.W.*, O'Doherty, J.P., and Shimojo, S. Neural computations mediating one-shot learning in the human brain. PLOS Biology 13(4): e1002137 (2015).
    (synopsis "How one-shot learning unfolds in the brain" by Weaver, J.)

  11. O'Doherty, J.P., Lee, S.W., McNamee, D. The structure of reinforcement-learning mechanisms in the human brain. Current Opinion in Behavioral Sciences 1, 94-100 (2015).

  12. Lee, S.W.*, Shimojo, S., and O'Doherty, J.P. Neural computations underlying arbitration between model-based and model-free learning. Neuron 81, 687-699 (2014).
    (front cover; preview "Decisions about decisions" by Yoshida, W. and Seymour, B.)

  13. Lee, S.W.*, Prenzel, O., and Bien, Z. Applying human learning principles to user-centered IoT systems. IEEE Computer 46, 46-52 (2013). (cover feature)

  14. Han, J.-S., Lee, S.W.*, and Bien, Z. Feature subset selection using separability index matrix. Information Sciences 223, 102-118 (2013).

  15. Isik, L., Leibo, J.Z., Mutch, J., Lee, S.W., and Poggio, T. A hierarchical model of peripheral vision. MIT CSAIL Technical Report, MIT-CSAIL-TR-2011-031 (2011).

  16. Jeon, M., Lee, S.W., and Bien, Z. Hand gesture recognition using multivariate fuzzy decision tree and user adaptation. International Journal of Fuzzy System Applications 1, 15-31 (2011).

  17. Bien, Z. and Lee, S.W. Learning structure of human behavior patterns in a smart home system. Advances in Intelligent and Soft Computing 82, 1-15 (2010).

  18. Lee, S.W., Kim, Y.S., and Bien, Z. A non-supervised learning framework of human behavior patterns based on sequential actions. IEEE Transactions on Knowledge and Data Engineering 22, 479-492 (2010).

  19. Lee, S.W. and Bien, Z. Representation of a fisher criterion function in a kernel feature space. IEEE Transactions on Neural Networks 21, 333-339 (2010).

  20. Lee, S.W., Kim, Y.S., Park, K.-H., and Bien, Z. Iterative bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled. Information Sciences 180, 325-340 (2010).

  21. Grigorescu, S.M., Lee, S.W., and Ristic-Durrant, D. Robust object recognition in service robotics. Proceedings of 30th Colloquium of Automation (2009).

  22. Song, J.-H., Jung, J.-W., Lee, S.W., and Bien, Z. Robust EMG pattern recognition to muscular fatigue effect for powered wheelchair control. Journal of Intelligent & Fuzzy Systems 20, 3-12 (2009).

  23. Prenzel, O., Lee, S.W., Bien, Z., and Graeser, A. A study on the application of the software framework MASSiVE in KAIST's intelligent sweet home system. International Journal of Assistive Robotics and Mechatronics 9 (2008).

  24. Jeon, M., Do, J.-H., Lee, S.W., Park, K.-H., and Bien, Z. A personalized hand gesture recognition system using soft computing techniques. Journal of Korea Institute of Intelligent Systems 18, 53-59 (2008). (written in Korean)

  25. Bien, Z., Han, J.-S., and Lee, S.W. Feature subset selection of biosignals for rehabilitation system. Proceedings of 28th Colloquium of Automation (2007).

  26. Kim, S., Jeon, M., Lee, S.W., Park, K.-H., and Bien, Z. Development of assistive software for disabled and aged people based on user characteristics - unified user interface for special work chair. Journal of the Institute of Electronics Engineers of Korea 44 (2007). (written in Korean)

  27. Kim, Y.S., Lee, S.W., Kang, S., Baek, Y.S., Hwang, S. and Bien, Z. Supervised IAFC neural network based on the fuzzification of learning vector quantization. Lecture Notes in Computer Science 4253, 248-254 (2006).

  28. Lee, S.W., Kim, D.-J., Kim, Y.S., and Bien, Z. Gabor wavelet neural network-based facial expression recognition system. Journal of Fuzzy Logic and Intelligent System 16, 1-7 (2006). (written in Korean)

  29. Lee, S.W., Kim, D.-J., Kim, Y.S., and Bien, Z. Training of feature extractor via new cluster validity - application to adaptive facial expression recognition. Lecture Notes in Computer Science 3864, 542-547 (2005).

  30. Kwon, Y.-J., Kim, D.-J., Lee, S.W., and Bien, Z. Development of half-mirror interface system and its application for ubiquitous environment. Journal of Control, Automation and System Engineering 11, 1-7 (2005). (written in Korean)


Peer-reviewed proceedings / posters

  1. 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”, in The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019.

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

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

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

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

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

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

  8. 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,” in Computational and Systems Neuroscience (COSYNE), 2019.

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

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

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

  12. 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,” in Proceeding of KIIS Fall Conference, 2018, p. 2. (written in Korean)

  13. 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,” in AAAI 2018 Fall Symposium, 2018.

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

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

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

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

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

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

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

  21. 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,” in Proceeding of IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), 2018, pp. 1–5.

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

  23. 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,” in Proceeding of IEEE/IEIE International Conference on Consumer Electronics (ICCE) Asia, 2018.

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

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

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

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

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

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

  30. M. R. Song and S. W. Lee*, “Meta BCI : Hippocampus-Striatum Network Inspired Architecture Towards Flexible BCI,” in Proceeding of the 6th International Winter Conference on Brain-Computer Interface (IEEE BCI 2018), 2018, pp. 0–2. (Oral presentation)

  31. D. Kim and S. W. Lee*, “Context-dependent meta-control for reinforcement learning using a Dirichlet process Gaussian mixture model,” in Proceeding of the 6th International Winter Conference on Brain-Computer Interface (IEEE BCI 2018), 2018, pp. 0–2.

  32. D. Kim, C. Weston, and S. W. Lee*, “EEG-based classification of learning strategies : model-based and model-free reinforcement learning,” in Proceeding of the 6th International Winter Conference on Brain-Computer Interface (IEEE BCI 2018), 2018, pp. 2–4.

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

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

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

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

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

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

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

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

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

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

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

  44. S. W. Lee*, “Bidirectional transformation between dominant cortical neural activities and phase difference distributions,” in The 25th Annual Computational Neuroscience Meeting, 2016.

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

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

    == Before joining KAIST  ==

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

  48. 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.

  50. 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.

  51. 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.

  52. 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.

  53. 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.

  54. 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.

  55. 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.

  56. 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.

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

  58. 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.

  59. 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.

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

  61. 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.

  62. 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)

  63. 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.

  64. 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.

  65. 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.

  66. 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.

  67. 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.

  68. 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.

  69. 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)

  70. 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)

  71. 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.

  72. 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.

  73. 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.

  74. 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)

  75. D.-J. Kim, S. W. Lee, and Z. Bien, “A Personalized Facial Expression Recognition System using Model Selection/Feature Selection,” in Proceedings of JSCI2006, 2006, pp. 197–201. (written in Korean)

  76. S. W. Lee, D.-J. Kim, Y. S. Kim, and Z. Bien, “Real-time Facial Expression Recognition System,” in Proceedings of JSCI2006, 2006, pp. 192–193. (written in Korean)

  77. 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.

  78. 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.

  79. 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)

  80. 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.

  81. 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.

  82. 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.

  83. 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.

  84. 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)

  85. 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)

  86. 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)

  87. 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.

  88. 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.

  89. 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)

  90. 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)

  91. 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.

  92. 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.

  93. 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)

  94. 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)

  95. 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.

  96. 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)

  97. 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.