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Real-Time Change Point Detection in Molecular Dynamics Streaming Data
SessionPoster Reception
DescriptionThe uniform sampling of molecular dynamics (MD) simulations may not accurately capture crucial scientific events. Deep learning approaches are being developed to detect these events within streaming data but can take significant resources on large datasets (PB+). To address these limitations, we propose a solution based on streaming manifold learning, specifically the Kernel CUSUM (KCUSUM) algorithm. By leveraging KCUSUM, we can overcome the limitations of uniform sampling in MD simulations, as it compares incoming data with samples from a reference distribution. It utilizes a statistic derived from the Maximum Mean Discrepancy (MMD) non-parametric testing framework. This algorithm has been tested in various use cases, demonstrating its ability to significantly reduce data rates without missing important scientific events.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
Research Posters
Scientific Visualization & Data Analytics Showcase
TimeTuesday, 14 November 20235:15pm - 7pm MST
LocationDEF Concourse
TP