I am a PhD candidate in Computer Engineering at USC, supervised by Prof. Viktor Prasanna. My main research goal is to improve the scalability, accuracy and efficiency of large scale graph learning. Towards the goal, during my PhD, I design new models, training/inference algorithms and hardware systems for Graph Neural Networks; after graduation, I will join Facebook/Meta AI as a research scientist to further explore practical solutions in web-scale social recommendation.
Beyond graph learning, I am also generally interested in solving memory and computation intensive problems by algorithm-architecture mapping approaches. In my initial years of PhD, I accelerated the computation of Convolutional Neural Networks and graph analytics via parallelization on heterogeneous platforms (GPU, CPU and FPGA).
PhD in Computer Engineering, 2022
University of Southern California
Bachelor of Engineering, 2016
University of Hong Kong
Old GNN models + New data perspective = Surpassing 1-WL, Avoiding oversmoothing & Overcoming neighborhood explosion
Official code: https://github.com/facebookresearch/shaDow_GNN
PyTorch Geometric implementation: https://pytorch-geometric.readthedocs.io/en/latest/modules/loader.html#torch_geometric.loader.ShaDowKHopSampler
Deep Graph Library implementation: https://docs.dgl.ai/en/latest/_modules/dgl/dataloading/shadow.html