Student Internship for Research at JMU CISE College

Worked on NSF-funded project analyzing aerosol effects on urban lightning patterns across U.S. cities; presented Spatiotemporal-DBSCAN: A Density-Based Clustering Method for Analyzing Spatiotemporal Ground-Lighting Datasets at IPCCC 2024 and CAPWIC 2025. Processed and cleaned over 2 million lightning data points using Python (Pandas, NumPy, Scikit-learn) across three cities. Proposed and implemented an algorithm reducing runtime from O(n^2) to O(n log n), improving processing time by 60% and increasing result accuracy by 45%. Collaborated with interdisciplinary teams to integrate computational models with geospatial analysis.