Federal Grant Award Summary

Temple University received a $160K Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), awarded July 15, 2026, with completion targeted for June 30, 2029. This collaborative research initiative develops statistical and machine learning methods for analyzing multivariate multi-replication point process data—datasets that record when events occur over time. The project addresses critical analytical challenges inherent in large-scale event-time data, including irregular event occurrence patterns, latent underlying structures, and simultaneous analysis of multiple event types across numerous subjects. Deliverables include three complementary classes of analytical models: generalized additive models for examining associations between scalar responses and high-dimensional point process predictors; graphical models for estimating dependence networks among multiple point processes through latent random intensity functions; and structural equation models for learning directional influences among event types.

The research has direct applicability to electronic health records analysis and neuroscience applications, with potential to improve clinical knowledge discovery and provide new insights into neuronal activity and brain function. Beyond research outputs, the project contributes to workforce development through research-based training, mentoring, outreach activities, and freely available software resources made accessible to the broader scientific community. This work advances the mathematical and computational foundations of event-time data analysis, positioning Temple University as a contributor to machine learning and artificial intelligence advancement in data science methodologies.