A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization. - Farshud Sorourifar; Georgios Makrygirgos; Ali Mesbah 002; Joel A. Paulson (CoRR, 2020)

Efficient Robust Global Optimization for Simulation-based Problems using Decomposed Gaussian Processes: Application to MPC Calibration. - Akshay Kudva; Farshud Sorourifar; Joel A. Paulson (ACC, 2022)

Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints. - Joel A. Paulson; Farshud Sorourifar; Ankush Chakrabarty (CDC, 2022)

A Tutorial on Derivative-Free Policy Learning Methods for Interpretable Controller Representations. - Joel A. Paulson; Farshud Sorourifar; Ali Mesbah 002 (ACC, 2023)

LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems. - Joel A. Paulson; Farshud Sorourifar; Christopher R. Laughman; Ankush Chakrabarty (ACC, 2023)

Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems. - Farshud Sorourifar; Joel A. Paulson; Ye Wang 001; Rien Quirynen; Christopher R. Laughman; Ankush Chakrabarty (CCTA, 2024)

Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces. - Farshud Sorourifar; Thomas Banker; Joel A. Paulson (CoRR, 2024)

TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery. - Madhav Muthyala; Farshud Sorourifar; Joel A. Paulson (CoRR, 2024)

SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond. - Madhav Muthyala; Farshud Sorourifar; You Peng; Joel A. Paulson (CoRR, 2025)