A GPU Server for Integration of Machine Learning in Mathematics and Statistics Research and Training

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We are requesting funds for a GPU server to provide critical computing power to support current and future research and training in Data Science for the Department of Mathematics and Statistics at the University of Calgary. The breakthrough of Machine Learning (ML) into both academic research, and industrial applications, has fundamentally changed the state-of-the-art of academic research, as well as ordinary citizens' lives. The integration of ML has become a trend in many fields. Moreover, there is also a growing population of trainees (among academic undergraduate and graduate students and professional continuing learners) who are enthusiastic to comprehend ML techniques in our research laboratories. Significant leverages for researchers to meet this need include modern ML libraries such as TensorFlow and PyTorch, which allow for the simple and prompt utilization of established ML tools. To embrace this exciting and ongoing data-oriented paradigm shift, the Department of Mathematics and Statistics has formed a Data Science group for ML research and training. It focuses on ML development tailoring to existing strengths in Biostatistics, Mathematical finance, High-dimensional statistical modeling, Neural network characterization, and Earth science. The applicants, an NSERC-funded diverse team of eleven (11) researchers representing this Data Science group, are integrating ML into their established research programs, supporting trainees from various backgrounds to give them experience and a knowledgebase aligning with their career goals. A roadblock to enacting the impact of this Data Science initiative is the lack of appropriate computational infrastructure tailoring to modern ML techniques. As such, we are requesting the support to acquire a GPU server with current mainstream specifications, which will allow for prompt deployment and execution of ML models. This equipment will be open to all researchers and trainees in the Math/Stats Department, providing them hands-on experiences in building and implementing ML models. Additionally, for non-ML computational tasks, the GPU server will also provide massive parallel computing power via a user-friendly interface, relieving researchers from writing code to manually coordinate multi-threads tasks (e.g., using OpenMP or MPI). The proposal is fully supported by the University of Calgary IT team, who will provide long-term storage, networking, maintenance, and user training. In summary, by supporting ML-focused and non-ML parallel computations, this infrastructure will benefit the research and HQP training in the Department, providing broader and better job opportunities for trainees in our graduate and undergraduate programs. When utilizing the equipment, actionable and measurable procedures will be implemented to facilitate a high degree of EDI in the department.