An Adjoint Based Implementation of the Parareal Algorithm. - Vishwas Rao; Adrian Sandu (ICCS, 2012)

Dynamic Sensor Network Configuration in InfoSymbiotic Systems Using Model Singular Vectors. - Adrian Sandu; Alexandru Cioaca; Vishwas Rao (ICCS, 2013)

A Sampling Approach for Four Dimensional Data Assimilation. - Ahmed Attia; Vishwas Rao; Adrian Sandu (DyDESS, 2014)

A Posteriori Error Estimates for DDDAS Inference Problems. - Vishwas Rao; Adrian Sandu (ICCS, 2014)

Emulation to simulate low-resolution atmospheric data. - Vishwas Rao; Rick Archibald; Katherine J. Evans (Int. J. Comput. Math., 2014)

An adjoint-based scalable algorithm for time-parallel integration. - Vishwas Rao; Adrian Sandu (J. Comput. Sci., 2014)

Parallel Solution of DDDAS Variational Inference Problems. - Vishwas Rao; Adrian Sandu (ICCS, 2015)

A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation. - Ahmed Attia; Vishwas Rao; Adrian Sandu (CoRR, 2015)

A Posteriori Error Estimates for the Solution of Variational Inverse Problems. - Vishwas Rao; Adrian Sandu (SIAM/ASA J. Uncertain. Quantification, 2015)

A time-parallel approach to strong-constraint four-dimensional variational data assimilation. - Vishwas Rao; Adrian Sandu (J. Comput. Phys., 2016)

Robust Data Assimilation Using L1 and Huber Norms. - Vishwas Rao; Adrian Sandu; Michael Ng 001; Elias David Niño Ruiz (SIAM J. Sci. Comput., 2017)

A Learning-Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models. - Azam S. Zavar Moosavi; Vishwas Rao; Adrian Sandu (ICCS (4), 2019)

Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. - Romit Maulik; Vishwas Rao; Sandeep Madireddy; Bethany Lusch; Prasanna Balaprakash (CoRR, 2019)

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators. - Nick Alger; Vishwas Rao; Aaron Myers; Tan Bui-Thanh; Omar Ghattas (SIAM J. Sci. Comput., 2019)

A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities. - Vishwas Rao; Romit Maulik; Emil M. Constantinescu; Mihai Anitescu (ICCS (6), 2020)

Efficient computation of extreme excursion probabilities for dynamical systems. - Vishwas Rao; Mihai Anitescu (CoRR, 2020)

Data-Driven Deep Learning Emulators for Geophysical Forecasting. - Varuni Katti Sastry; Romit Maulik; Vishwas Rao; Bethany Lusch; S. Ashwin Renganathan; Rao Kotamarthi (ICCS (5), 2021)

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models. - Azam S. Zavar Moosavi; Vishwas Rao; Adrian Sandu (J. Comput. Sci., 2021)

Efficient Computation of Extreme Excursion Probabilities for Dynamical Systems through Rice's Formula. - Vishwas Rao; Mihai Anitescu (SIAM/ASA J. Uncertain. Quantification, 2021)

Frequency Recovery in Power Grids using High-Performance Computing. - Vishwas Rao; Anirudh Subramanyam; Michel Schanen; Youngdae Kim; Ignas Satkauskas; Mihai Anitescu (ICPP Workshops, 2022)

Learning the Evolution of Correlated Stochastic Power System Dynamics. - Tyler E. Maltba; Vishwas Rao; Daniel Adrian Maldonado (CoRR, 2022)

LaplaceInterpolation.jl: A Julia package for fast interpolation on a grid. - Vishwas Rao; Charlotte A. L. Haley; Mihai Anitescu (J. Open Source Softw., 2022)

Optimization under Rare Chance Constraints. - Shanyin Tong; Anirudh Subramanyam; Vishwas Rao (SIAM J. Optim., 2022)

CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis. - S. Ashwin Renganathan; Vishwas Rao; Ionel Michael Navon (J. Comput. Phys., 2023)

Scalable Multi-Period AC Optimal Power Flow Utilizing GPUs with High Memory Capacities. - Sungho Shin; Vishwas Rao; Michel Schanen; Daniel Adrian Maldonado; Mihai Anitescu (OSMSES, 2024)

Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation. - Amit N. Subrahmanya; Vishwas Rao; Arvind K. Saibaba (CoRR, 2024)

Bayesian D-Optimal Experimental Designs via Column Subset Selection: The Power of Reweighted Sensors. - Srinivas Eswar; Vishwas Rao; Arvind K. Saibaba (CoRR, 2024)

Optimal sensor placement under model uncertainty in the weak-constraint 4D-Var framework. - Alen Alexanderian; Hugo Díaz; Vishwas Rao; Arvind K. Saibaba (CoRR, 2025)

Weakly-Constrained 4D Var for Downscaling with Uncertainty using Data-Driven Surrogate Models. - Philip Dinenis; Vishwas Rao; Mihai Anitescu (CoRR, 2025)

Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models. - Haoyuan Chen; Emil M. Constantinescu; Vishwas Rao; Cristiana Stan (CoRR, 2025)

Structured Column Subset Selection for Bayesian Optimal Experimental Design. - Hugo Díaz; Arvind K. Saibaba; Srinivas Eswar; Vishwas Rao; Zichao Wendy Di (CoRR, 2025)

Scientific Applications Leveraging Randomized Linear Algebra. - Vivak Patel; Daniel Adrian Maldonado; Maksim Melnichenko; Nathaniel Pritchard; Vishwas Rao; Elizaveta Rebrova; Sriram Sankararaman (CoRR, 2025)