Congratulation!! Wonjun is granted a Student Award based on his abstract paper submission to the 2020 International BCI Meeting.
Welcome to the Machine Intelligence Lab
The Machine Intelligence Lab (MILAB) at Korea University is directed by Professor Heung-Il Suk.
We are tackling fundamentally open problems in developing biologically plausible machine/deep learning algorithms for various applications including medical/computer vision, brain-computer interfaces, healthcare, and neuroinformatics.
Research Areas

Machine/Deep Learning
Developing novel deep learning methods that can integrate multiple neuroimaging modalities

AI in Neuroinformatics
Developing machine-learning algorithms that can estimate the underlying functional patterns from fMRI images and utilize the estimated information for disease diagnosis/prognosis

AI in Medical/Computer Vision
Developing various applications for medical/computer vision tasks, where the objective is to achieve state-of-the-art performance with human-level interpretability in mind

AI in Brain-Computer Interface
Developing BCI-oriented but paradigm-independent algorithms that intelligently remove artifacts/noise and learn a user-specific brain signal patterns by utilizing their resting-state signals

AI in Healthcare
Developing deep architectures for disease phenotying, future patients’ state prediction, missing value imputation, and uncertainty quantification from Electronic Health Records (EHR) for better understanding and investigation of the underpinning phenomena in healthcare observations
Lab’s latest news
[KU Graduate Student Achievement Award] 고원준, 전은지 수상
고원준, 전은지 연구원이 고려대 최우수대학원생에게 수여하는 “KU Graduate Student Achievement Award”를 수상하였습니다.“KU Graduate Student Achievement Award” 는 본교 대학원생의 학문적, 사회적 업적을...
Accepted to IEEE Computational Intelligence Magazine
Congratulations!!! Wonjun’s work on “Multi-Scale Neural Network for EEG Representation Learning in BCI” is accepted to IEEE Computational Intelligence Magazine...
Accepted to IEEE-CYB
Congratulations!!! Wisnu’s work on “Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series” is accepted to IEEE Transactions on Cybernetics.