Yi Hao Chan

Welcome! 😄

I’m currently a postdoctoral fellow in the College of Computing and Data Science, Nanyang Technological University, Singapore. I’ve completed my undergraduate degrees (Business and Computing, with specialisations in Business Analytics, Data Science & Analytics, AI) and PhD (Computer Science, specifically in brain imaging, multimodal learning and explainable AI) in NTU as well.

My research interests are within the intersection of Machine Learning and Neuroscience. Currently, I’m focused on Temporal Graph Interpretability. Specifically, I am interested in modelling multimodal datasets of human brain activity to identify salient spatiotemporal patterns, improving our understanding of structure-function relationships (in disorder states).

A long term goal I have is to improve learning and memory in both humans and machines, in the process understanding how we can better treat learning and neurodevelopmental disorders, as well as atypical neurodegeneration. Outside of research, I am interested in developing DS/AI solutions unique to SG and applying temporal graphs to other domains (e.g. quantitative finance).

Recent Updates

  • Aug 2025: Our review on discovering robust fMRI biomarkers of psychiatric disorders via GNN was accepted by the journal Neuroimage
  • July 2025: Our paper on disentangling functional connectivity changes associated with clinical severity and cognitive phenotypes of Schizophrenia was accepted by the journal Communications Biology
  • May 2025: Our paper on meta-analysis guided GNNs for neurological disorder prediction tasks was accepted by MICCAI (highlighted paper)
  • Feb 2025: Our paper on an interpretable GCN model that incorporates both modality specific and cross-modal interactions was accepted by the journal Medical Image Analysis
  • Jan 2025: 2 US patent applications on brain lesion and tissue segmentation submitted
  • Dec 2024: Our paper on decoding cross-modal interactions between sMRI and gene expression for Alzheimer’s disease was accepted by ICASSP
  • Sep 2024: Our paper on evaluating the robustness of XAI algorithms for biomarker discovery from functional connectomes was accepted by IEEE BHI (poster)
  • May 2024: Our paper on learning modularity-guided structure-function interactions was accepted by MICCAI (early accept)
  • Apr 2024: Presented our papers @ ICASSP 2024, held in Seoul (oral)
  • Apr 2024: Our paper on a 2-stage approach to ICH segmentation was accepted by the journal IEEE Access
  • Jan 2024: 2 abstracts accepted @ ISMRM 2024
    • Ensemble of GNNs for decoding dFC in ADHD (poster)
    • Interpretable GNN for classification of Parkinson’s disease using multimodal connectomes (oral)
  • Jan 2024: 2 abstracts accepted @ OHBM 2024
    • Analysing dFC biomarkers of ADHD subtypes (poster)
    • Structure-function interactions associated with fluid cognition via GNNExplainer (poster)
  • Dec 2023: 2 papers accepted @ ICASSP 2024
    • Generating subtype-specific biomarkers of Alzheimer’s disease via SplitGNN (oral)
    • Multimodal GNN for predicting fluid cognition (oral)
  • Nov 2023: Our paper on discovering site-specific salient features and site-invariant biomarkers of Schizophrenia was accepted by the journal Scientific Reports