Research Areas


Big Data Anlysis
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Big Data in Biomedical Research focuses on model adaptability and data efficacy through various deep learning techniques.

Domain Adaptation Multi-modality model fusion Meta learning-based Cross-domain Generalization Generative model-based Synthetic data augmentation
Brain Disease Diagnosis
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Our team conduct comprehensive research into a range of complex neurological disorders, working to advance the field of diagnosis techniques using various deep learning techniques.

Functional brain network analysis (MDD/MCI) Unsupervised brain tumor segmentation Epilepsy prediction Functional MRI Denoising
Brain-Computer Interface
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Our team applies deep learning methods to analyze EEG signals in Brain-Computer Interfaces (BCIs). We aim to provide personalized solutions for individual users, while also working on frameworks that have broader applicability.

Brain-Computer Interface
Molecular AI
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Our team excels in the field of Molecular AI, unraveling the intricacies of molecular behaviors through innovative research. We are now pioneering research in Molecular Optimization, aiming to automate the refinement of molecular structure.

Molecular AI
Cell Image Analysis
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To gain a deeper understanding of brain function, our team aimed to enhance the accuracy of calcium signals by refining the processing of calcium imaging data. We employ deep learning techniques for the processing of calcium imaging data.

Cell Image Analysis
Reinforcement Learning
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Our team leverages reinforcement learning for personalized medical decision-making, continuous learning, and research in areas like ROI and EEG-based medical traits, with a focus on enhancing care and patient outcomes.

Multi-agent reinforcement learning in decision making Feature selection in Neuroimaging