| Phase II monitoring of nonlinear profiles A Vaghefi, SD Tajbakhsh, R Noorossana Communications in Statistics—Theory and Methods 38 (11), 1834-1851, 2009 | 122 | 2009 |
| An artificial neural network approach to multiple-response optimization R Noorossana, S Davanloo Tajbakhsh, A Saghaei The International Journal of Advanced Manufacturing Technology 40, 1227-1238, 2009 | 84 | 2009 |
| Geodesic Gaussian processes for the parametric reconstruction of a free-form surface E Del Castillo, BM Colosimo, SD Tajbakhsh Technometrics 57 (1), 87-99, 2015 | 60 | 2015 |
| A Bayesian approach to sequential optimization based on computer experiments SD Tajbakhsh, E Del Castillo, JL Rosenberger Quality and Reliability Engineering International 31 (6), 1001-1012, 2015 | 14 | 2015 |
| On the theoretical guarantees for parameter estimation of gaussian random field models: A sparse precision matrix approach SD Tajbakhsh, NS Aybat, E Del Castillo Journal of Machine Learning Research 21 (217), 1-41, 2020 | 12* | 2020 |
| Generalized sparse precision matrix selection for fitting multivariate gaussian random fields to large data sets SD Tajbakhsh, NS Aybat, E Del Castillo Statistica Sinica, 941-962, 2018 | 7 | 2018 |
| Riemannian stochastic variance-reduced cubic regularized Newton method D Zhang, SD Tajbakhsh arXiv preprint arXiv:2010.03785, 2020 | 6 | 2020 |
| Sparse precision matrix selection for fitting gaussian random field models to large data sets S Davanloo Tajbakhsh, NS Aybat, E Del Castillo arXiv preprint arXiv:1405.5576, 2015 | 5 | 2015 |
| Fitting arma time series models without identification: A proximal approach Y Liu, SD Tajbakhsh International Conference on Artificial Intelligence and Statistics, 3835-3843, 2024 | 4 | 2024 |
| Riemannian stochastic variance-reduced cubic regularized Newton method for submanifold optimization D Zhang, S Davanloo Tajbakhsh Journal of Optimization Theory and Applications 196 (1), 324-361, 2023 | 3 | 2023 |
| A first-order optimization algorithm for statistical learning with hierarchical sparsity structure D Zhang, Y Liu, S Davanloo Tajbakhsh INFORMS Journal on Computing 34 (2), 1126-1140, 2022 | 3 | 2022 |
| Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions Y Liu, SD Tajbakhsh, AJ Conejo International Journal of Forecasting 37 (2), 812-824, 2021 | 3 | 2021 |
| Multi-scale biological and physical modelling of the tumour micro-environment RF Kunz, BJ Gaskin, Q Li, S Davanloo-Tajbakhsh, C Dong Drug Discovery Today: Disease Models 16, 7-15, 2015 | 3 | 2015 |
| Important issues in multiple response optimization SD Tajbakhsh, R Noorossana 4th International Management Conference 1, 2006 | 3 | 2006 |
| Riemannian Stochastic Gradient Method for Nested Composition Optimization D Zhang, SD Tajbakhsh arXiv preprint arXiv:2207.09350, 2022 | 1 | 2022 |
| Stochastic Composition Optimization of Functions Without Lipschitz Continuous Gradient Y Liu, S Davanloo Tajbakhsh Journal of Optimization Theory and Applications 198 (1), 239-289, 2023 | | 2023 |
| Adaptive Stochastic Optimization Algorithms for Problems with Biased Oracles Y Liu, SD Tajbakhsh arXiv preprint arXiv:2306.07810, 2023 | | 2023 |
| Supplementary Material: Geodesic Gaussian Processes for the Parametric Reconstruction of a Free-Form Surface E Del Castillo, BM Colosimo, S Davanloo Quality Engineering, 2015 | | 2015 |
| On convex optimization methods for fitting spatial statistical models to large data sets SD Tajbakhsh The Pennsylvania State University, 2015 | | 2015 |
Enrique del CastilloDistinguished Professor, Industrial Engineering & Statistics depts., Penn State UniversityE-mail confirmado em psu.edu
