Welcome! :)

I’m Yihao, a 4th year PhD candidate in Nanyang Technological University, Singapore. I’ve done my undergraduate degrees (Business and Computer Science) in NTU as well, with specializations in Business Analytics, Data Science & Analytics and Artificial Intelligence.

My research interests are within the intersection of Computer Science and Neuroscience, specifically Multiview Representation Learning and Multimodal Neuroimaging Analysis. My key interest is to contribute towards a better understanding of structure-function relationships in the human brain, for both normal function and various disease 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 describe and treat learning and neurodevelopmental disorders, as well as atypical neurodegeneration.

Recent Updates

Dec 2023: 2 papers accepted @ ICASSP 2024 (Subtype-specific biomarkers for Alzheimer’s disease via GNN, Multimodal GNN for predicting fluid cognition)!
Nov 2023: Our paper on discovering site-specific salient features and site-invariant biomarkers of Schizophrenia was accepted by Scientific Reports
Aug 2023: Our paper on subtype-specific biomarkers for autism was accepted by IEEE BHI (poster)
Aug 2023: Our paper on a network-based approach for modelling sMRI and PET scans was accepted by the journal Computers in Biology and Medicine
Jul 2023: Our paper on modelling multimodal connectomes using brain graphs and population graphs, was accepted by IEEE BHI (Rapid-Fire presentation)
Oct 2022: Presented our paper on SHRED (site-invariant biomarker discovery) at MICCAI 2022 (poster)
Jun 2022: Our paper on generating site-invariant disease biomarkers via data harmonisation and semi-supervised learning (SHRED) was accepted by MICCAI, see you in Singapore! :)
May 2022: Our paper on combining multimodal neuroimaging data with multi-omics data (JOIN-GCLA) was accepted by the journal Frontiers in Neuroscience
Oct 2020: Presented our paper on SPOTS (brain state classification) at the MLCN workshop (held in conjunction with MICCAI)
Aug 2020: Our paper on a customised architecture, Salient Patterns Over Time and Space (SPOTS), for brain state classification using task-fMRI scans, was accepted by MLCN
Apr 2020: Our paper on a node removal algorithm (LEAN + CLIP) for biomarker discovery via deep learning models was accepted by the journal Neurocomputing
Oct 2019: Our paper on an iterative feature selection algorithm for AD/MCI/CN classification was presented at MICCAI (Oral)