Data-science algorithms and models changed the way we search for information on products and services, make payments, procure and trade, and along the way, changed how firms use individual-level consumer data,and how business transactions are created, documented, regulated, and analyzed….

The objective is to obtain optimal routing solutions that would, as much as possible, adhere to a set of specified requirements after the uncertainty is realized. These problems include finding an optimal routing solution to meet the soft time window requirements at a subset of nodes when the travel…

Parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for…

The design of this method combines an inexact 2-block majorized semi-proximal ADMM and the recent advances in the inexact symmetric Gauss-Seidel (sGS) technique for solving a multi-block convex composite quadratic programming whose objective contains a nonsmooth term involving only the first block-variable. One distinctive feature of our proposed method…

New approach can help organizations scale their data science efforts with artificial data and crowdsourcing.Although data scientists can gain great insights from large data sets — and can ultimately use these insights to tackle major challenges — accomplishing this is much easier said than done. Many such efforts are…

New approach can help organizations scale their data science efforts with artificial data and crowdsourcing.Although data scientists can gain great insights from large data sets — and can ultimately use these insights to tackle major challenges — accomplishing this is much easier said than done. Many such efforts are…