Vipin Kumar

TL;DR: An efficient parallel formulations of dense iterative solvers based on hierarchical approximations for solving the integral form of Laplace equation is presented and the impact of various parameters on the accuracy and performance of the parallel solver is studied.

TL;DR: This paper presents a framework for learning robust predictive models in real-world heterogeneous datasets which lack sufficient number of training samples, and shows that the framework captures meaningful information about heterogeneity in the data, improves prediction performance by addressing data heterogeneity, and is robust to over-fitting in the presence of limited training data.

TL;DR: This work improves upon the state-of-the-art connected component eddy monitoring algorithms to track eddies globally and employs a physically-consistent convexity requirement on eddies based on theoretical and empirical studies to improve the accuracy and computational complexity of the method.

TL;DR: In this paper, the authors analyzed 17 yr (1982-1998) of net carbon flux predictions from a simulation model based on satellite observations of monthly vegetation cover and found that although the terrestrial ecosystem sink for atmospheric CO2 for the Eurasian region has been fairly consistent at between 0.3 and 0.6 Pg C per year since 1988, high interannual variability in net ecosystem production (NEP) fluxes can be readily identified at locations across the continent.