Madhav V. Marathe

TL;DR: This formulation models a number of problems arising in facility location, statistical clustering, pattern recognition, and also a processor allocation problem in multiprocessor systems.

TL;DR: It is shown that the framework for computational problems arising from electrical power networks due to the proposed deregulation of the electric utility industry in the USA, and applications such as real-time Internet services, come in a few variants, some efficiently solvable and many NP-hard; the latter also yield improved approximations for a family of packing integer programs.

TL;DR: EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets and offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging.

TL;DR: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information (TDEFSI) as discussed by the authors is an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks.

TL;DR: Adiga et al. as mentioned in this paper presented a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States.