Machine/Deep Learning

Developing novel deep learning methods that can integrate multiple neuroimaging modalities

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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

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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

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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

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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

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