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ORCIDFarshud Sorourifar
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2020 – today
- 2025
[c6]Qi Zhang, Styliani Avraamidou, Joel A. Paulson, Vyom Thakker, Zhenyu Wang, Leo Chiang, Birgit Braun, Tushar Rathi, Ankush Chakrabarty, Farshud Sorourifar, Wei-Ting Tang, Paola Munoz, Apoorva Sampat, France Guertin:
Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering. ACC 2025: 1725-1742- [i5]Madhav Muthyala, Farshud Sorourifar, You Peng, Joel A. Paulson:
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond. CoRR abs/2502.03367 (2025) - [i4]Madhav Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson:
Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design. CoRR abs/2512.17659 (2025) - 2024
- [c5]Farshud Sorourifar, Joel A. Paulson, Ye Wang, Rien Quirynen, Christopher R. Laughman, Ankush Chakrabarty:
Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems. CCTA 2024: 414-419 - [i3]Farshud Sorourifar, Thomas Banker, Joel A. Paulson:
Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces. CoRR abs/2401.01398 (2024) - [i2]Madhav Muthyala, Farshud Sorourifar, Joel A. Paulson:
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery. CoRR abs/2410.01752 (2024) - 2023
- [c4]Joel A. Paulson, Farshud Sorourifar, Christopher R. Laughman, Ankush Chakrabarty:
LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems. ACC 2023: 576-582 - [c3]Joel A. Paulson, Farshud Sorourifar, Ali Mesbah:
A Tutorial on Derivative-Free Policy Learning Methods for Interpretable Controller Representations. ACC 2023: 1295-1306 - 2022
- [c2]Akshay Kudva, Farshud Sorourifar, Joel A. Paulson:
Efficient Robust Global Optimization for Simulation-based Problems using Decomposed Gaussian Processes: Application to MPC Calibration. ACC 2022: 2091-2097 - [c1]Joel A. Paulson, Farshud Sorourifar, Ankush Chakrabarty:
Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints. CDC 2022: 123-129 - 2020
- [i1]Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah, Joel A. Paulson:
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization. CoRR abs/2011.11841 (2020)
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