Computing Workstations for Deep Learning on Graph-Structured Data

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With NSERC RTI's support, we will establish the Graph Neural Network Lab (GNN-Lab), the first deep learning laboratory at the University of Windsor with a special interest in applying deep neural networks on graph-structured data. GNN research is intractable without graphics processing units (GPUs) that accelerate neural network workloads, reducing elapsed time from months to days or even hours, dramatically increasing the feasibility of the research and ability to conduct and disseminate cutting-edge research in an efficient manner. The University of Windsor has already committed to supporting the growth of GNN research through the recent (2020) hire of a leading GNN Early Career Researcher, making the establishment of a GNN-Lab critical and urgent at this time. This proposal requests 8 GPU-enabled computer workstations to bootstrap GNN-Lab, supporting current and future GNN research at UWindsor. In line with 2019's launched of $125M Pan-Canadian AI Strategy by the Government of Canada to develop and lead AI in close collaboration with Canada's research institutes, our GNN-Lab fueled with the requested workstations, ignite Windsor's contribution in pushing forward the state-of-the-art in AI, and meanwhile helping local ecosystems and industrial partners. Specifically, the overarching objective of GNN-Lab is to perform cutting-edge research at the interface between graph theory, time series analysis, and bioinformatics to overcome the limitations of current studies in i) information retrieval, ii) social network analysis, and iii) drug design through three prime synergistic research programs A, B, and C. GNN-Lab will push forward the progress of less explored AI on GNN that finds immediate applications in building intelligent search engines for information retrieval using social information (Social IR) in research programs A and B, and productive pharmaceutical pipeline using drug repurposing in research program C. Using the requested GPU-enabled workstations, HQP will acquire training in and become adept at the application of GNN on social network analysis, information retrieval, and drug repurposing, putting students at the cutting edge of graph-structured data processing knowledge. GNN-Lab will see consistent engagement with industrial partners where research findings will be translated into working prototypes, providing a unique opportunity for HQP to see their efforts be applied in the industry while providing insights into the next career steps. HQP can then immediately be absorbed by the search engine job market, including Google as well as the pharmaceutical industry, including Pfizer Canada Inc.