Probabilistic Robust Small-Signal Stability Framework using Gaussian Process Learning. - Parikshit Pareek; Hung D. Nguyen 001 (CoRR, 2019)

Non-parametric Probabilistic Load Flow using Gaussian Process Learning. - Parikshit Pareek; Chuan Wang; Hung D. Nguyen 001 (CoRR, 2019)

Small-Signal Stability Constrained Optimal Power Flow: A Convexification Approach. - Parikshit Pareek; Hung D. Nguyen 001 (CoRR, 2019)

Gaussian Process Learning-based Probabilistic Optimal Power Flow. - Parikshit Pareek; Hung D. Nguyen 001 (CoRR, 2020)

Health-Focused Optimal Power Flow. - Logesh Kumar; Parikshit Pareek; Sivakumar Nadarajan; Souvik Dasgupta; Amit Gupta; Hung D. Nguyen 001 (ISGT, 2021)

A Convexification Approach for Small-Signal Stability Constrained Optimal Power Flow. - Parikshit Pareek; Hung Dinh Nguyen 001 (IEEE Trans. Control. Netw. Syst., 2021)

Optimal Steady-State Voltage Control Using Gaussian Process Learning. - Parikshit Pareek; Weng Yu; Hung Dinh Nguyen 001 (IEEE Trans. Ind. Informatics, 2021)

Graph-Structured Kernel Design for Power Flow Learning using Gaussian Processes. - Parikshit Pareek; Deepjyoti Deka; Sidhant Misra (CoRR, 2023)

Data-Efficient Power Flow Learning for Network Contingencies. - Parikshit Pareek; Deepjyoti Deka; Sidhant Misra (CoRR, 2023)

Scalable Probabilistic Optimal Power Flow for High Renewables Using Lite Polynomial Chaos Expansion. - Sel Ly; Parikshit Pareek; Hung Dinh Nguyen 001 (IEEE Syst. J., 2023)

Locating Critical Prosumers in P2P Dominant Grids Using State-Sensitivity Function. - Parikshit Pareek; Mohasha Isuru Sampath Lahanda Purage; Hung Dinh Nguyen 001; Yi Shyh Eddy Foo (IEEE Trans. Smart Grid, 2023)

A Convergence Predictor Model for Consensus-based Decentralised Energy Markets. - Y. S. Eddy Foo; Parikshit Pareek; L. P. M. I. Sampath; Hung D. Nguyen 001 (e-Energy, 2024)

Demystifying Quantum Power Flow: Unveiling the Limits of Practical Quantum Advantage. - Parikshit Pareek; Abhijith Jayakumar; Carleton Coffrin; Sidhant Misra (CoRR, 2024)

Optimization Proxies using Limited Labeled Data and Training Time - A Semi-Supervised Bayesian Neural Network Approach. - Parikshit Pareek; Kaarthik Sundar; Deepjyoti Deka; Sidhant Misra (CoRR, 2024)

Degradation-Infused Energy Portfolio Allocation Framework: Risk-Averse Fair Storage Participation. - Parikshit Pareek; L. P. M. I. Sampath; Anshuman Singh; Lalit Goel; Hoay Beng Gooi; Hung Dinh Nguyen 001 (CoRR, 2024)

A Stress-Cognizant Optimal Battery Dispatch Framework for Multimarket Participation. - Anshuman Singh; Parikshit Pareek; Mohasha Isuru Sampath Lahanda Purage; Lalit Goel; Hoay Beng Gooi; Hung Dinh Nguyen 001 (IEEE Trans. Ind. Informatics, 2024)

Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes. - Daniel Glover; Parikshit Pareek; Deepjyoti Deka; Anamika Dubey (CoRR, 2025)

Gaussian Processes in Power Systems: Techniques, Applications, and Future Works. - Bendong Tan; Tong Su; Yu Weng; Ketian Ye; Parikshit Pareek; Petr Vorobev; Hung D. Nguyen 001; Junbo Zhao 001; Deepjyoti Deka (CoRR, 2025)