Building upon the foundations of spatial statistics and seismology, I introduced a cluster merging strategy hinged on a density similarity threshold and spatial continuity. When two fitted lines intersected within a predetermined geospatial radius, a density comparison was executed. If the density difference was within a stipulated threshold, the clusters were merged to form a more expansive fault network. This data-driven approach resulted in a more nuanced and spatially-aware clustering model, thereby enhancing its predictive capabilities in seismic risk assessment. The novel algorithm not only increases the robustness of earthquake prediction models but also contributes to advancing the frontier of computational seismology.

In an interdisciplinary project leveraging machine learning, statistics, and geophysical sciences, I innovated upon the standard Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to improve earthquake fault detection along Earth's crust. Initially, DBSCAN was employed to identify fault clusters based on seismic data, applying metrics like fault density and geospatial orientation. Post-clustering, each cluster underwent Principal Component Analysis (PCA) for straight-line fitting, allowing for the quantification of the cluster's dominant geospatial orientation. The fitted lines served as directional vectors for spatial extrapolation, which laid the groundwork for a novel cluster-merging mechanism.

Exploring the complexities of high-dimensional regression by employing modern techniques like Ridge Regression and the Least Absolute Shrinkage and Selection Operator (LASSO). Through a systematic evaluation, we compare the efficacy of these methods in minimizing overfitting while maximizing predictive accuracy. Our approach encompasses the application of these techniques on complex datasets, with the results having significant implications for feature selection, model robustness, and predictive analytics in high-dimensional data environments.

Investigating the predictive capabilities of weather and financial market forecasts, scrutinizing their reliability and accuracy using machine learning models. The study compares the variables influencing each forecast type and explores any potential overlap, aiming to understand if one type of forecast can inform the other.

Utilizing machine learning algorithms to analyze the historic New York Horse Manure Crisis, exploring how alternative financial models could have expedited solutions. The study emphasizes the potential of machine learning in addressing complex socio-economic issues.

Examining the "weekend effect" in atmospheric chemistry during the COVID-19 lockdowns, focusing on shifts in air pollutant levels. Real-time data is leveraged to reveal how human activity and reduced mobility influenced air quality.