Successful application of deep learning to complex research problems often hinges on access to highly specialized computational hardware. This award will provide funds to purchase a system comprised of four highly effective, purpose-built deep learning nodes to be used for a wide array of deep learning applications. An additional purchase of two state-of-the-art, purpose-built deep learning nodes for high-dimensional, memory-hungry and/or data-rich applications is also enabled. The four “staging†nodes will permit development and testing prior to deployment on the two state-of-the-art nodes. The full six-node system will be used by members of the Deep Learning for Statistics, Astrophysics, Geoscience, Engineering, Meteorology and Atmospheric Science, Physical Sciences and Psychology (DL-SAGEMAPP) team and the broader community at the Pennsylvania State University. The team will provide access to the hardware to roughly 300 advanced undergraduate and graduate students each year, for use in courses in multiple departments that either focus on machine learning and artificial intelligence or include those topics in their curricula. The team will also host an annual multi-day hands-on workshop in deep learning. The workshop will welcome all students in the DL-SAGEMAPP team’s research areas but will advertise most heavily to students from underrepresented groups. Team members will also contribute deep learning sessions to one or more of the annual workshops for K-12 teachers held by Penn State’s Astronomy and Astrophysics Department, with the potential to convey deep learning ideas to grade-school students.
The goal is to create a cutting-edge, shared resource that supports a diverse set of researchers, allowing them to transform problems that are currently impractical or impossible to solve with existing computational resources into proverbial “low-hanging fruit.†The DL-SAGEMAPP team will harness the power of deep learning to tackle some of the most challenging problems in their respective areas of research. With purpose-built hardware and popular open software, the team will apply deep learning methodologies to problems with high dimensionality, high data volume, and/or requiring very complex network topologies; problems that would take too long to run or would be too large to fit in on-board memory for most standard hardware configurations. This team aims to boost the search for multimessenger astrophysical signals by improving the sensitivity and response time of flagship high-energy neutrino, gravitational wave, and wide-field survey observatories. The team will apply deep learning to the simulation and analysis of satellite and aerial data, leading to greater predictive power for impending volcanic activity and the build-up of sea ice in the Arctic, and greater acuity with cloud-shrouded ground targets. Deep learning techniques will be used to increase the accuracy of flood forecasting, improve the accuracy and turnaround time of molecular-level simulations, delve more deeply into the process of protein synthesis by mRNA, tackle the analysis of increasingly large data volumes from brain-implanted electrodes, and sharpen researchers’ understanding of the human visual system.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
