Research Scope
(Machine learning, Structure-aware Graph Machine Learning, LLMs)
- Foundations of Deep Learning Models: Exploring the core principles and advancements in deep learning models (e.g., LLMs and diffusion models).
- Interdisciplinary Applications: Applying machine learning techniques across different domains by leveraging REAL data (e.g., healthcare, computational neuroscience, earth systems, and solar physics).
- Spatio-Temporal Forecasting: Predicting future events based on spatial and temporal data.
- Causal Discovery: Identifying cause-and-effect relationships within data.
- Interpretability and Scalability: Enhancing the transparency and efficiency of machine learning models.
Recent Projects
Full publications can be found from here.
Hyperbolic Graph Embedding of MEG Brain Networks to Study Brain Alterations in Individuals With Subjective Cognitive Decline
This study develops a hyperbolic MEG brain network embedding framework to analyze early Alzheimer’s disease (SCD stage) by mapping brain networks into low-dimensional hyperbolic space.... Recent News
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