11 citations · 18 across the 8 of their papers we have counts for
8 papers
Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan +2
Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high…
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
Madhav R. Muthyala, Farshud Sorourifar, You Peng +1
Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data. Although a wide-variety of SR…
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
Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact represe…
Towards Efficient Quantum Computation of Molecular Ground State Energies using Bayesian Optimization with Priors over Surface Topology
Farshud Sorourifar, Mohamed Taha Rouabah, Nacer Eddine Belaloui +6
Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classic…
Bayesian Optimization Priors for Efficient Variational Quantum Algorithms
Farshud Sorourifar, Diana Chamaki, Norm M. Tubman +2
Quantum computers currently rely on a hybrid quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. Still, there are several challenges with V…
Surrogate optimization of variational quantum circuits
Erik J. Gustafson, Juha Tiihonen, Diana Chamaki +8
Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of qu…
Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces
Farshud Sorourifar, Thomas Banker, Joel A. Paulson
Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive s…
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization
Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah +1
The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, predicti…