Abhi develops statistical and machine learning methods for large spatial datasets as well as Bayesian models for multi-source epidemiological datasets.

Aki uses Bayesian methods and statistical computing to tackle methodological challenges in healthcare analytics and large-scale biomedical applications.

The increasing availability of large-scale geospatial and spatiotemporal data presents new opportunities and challenges for statistical modeling in environmental, technological, medical, and other complex areas, which increasingly rely on massive multivariate spatiotemporal datasets. Yet, Bayesian learning for such problems remains severely limited by computational bottlenecks and the lack of flexible modeling tools. Modern applications require methods that are adaptive and effective, but still computationally efficient, scalable to massive datasets, and capable of delivering reliable automated inference with principled uncertainty quantification and (possibly) minimal experienced human intervention. Classical Bayesian approaches, although theoretically appealing and offering rich inferential frameworks, often become computationally infeasible in data-rich environments, especially when confronted with massive datasets or dynamic, high-dimensional dependence structures. Existing approaches often fail to scale, leaving a gap between the theoretical richness of Bayesian inference and its practical deployment in data-rich applications. This thesis develops Bayesian transfer learning methodologies to address these challenges, enabling efficient information propagation and scalable inference across large spatial and spatiotemporal domains, providing a unified framework that merges distributional theory for matrix-variate models with computational innovations in Bayesian predictive stacking. Through extensive simulation experiments and data applications to global and satellite monitoring of vegetation indices, sea surface temperature, and land-atmospheric climate composition, the thesis also demonstrates the potential of Bayesian transfer learning to redefine spatial and spatiotemporal multivariate modeling, providing flexible, computationally efficient solutions that open the way for scalable, automated, and truly modern tools for geospatial learning in data-rich environments.