| Support vector machine based reliability analysis of concrete dams MA Hariri-Ardebili, F Pourkamali-Anaraki Soil Dynamics and Earthquake Engineering 104, 276-295, 2018 | 131 | 2018 |
| Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means F Pourkamali-Anaraki, S Becker IEEE Transactions on Information Theory, 2017 | 82 | 2017 |
| Simplified reliability analysis of multi hazard risk in gravity dams via machine learning techniques MA Hariri-Ardebili, F Pourkamali-Anaraki Archives of civil and mechanical engineering 18 (2), 592-610, 2018 | 58 | 2018 |
| An empirical evaluation of the t-sne algorithm for data visualization in structural engineering P Hajibabaee, F Pourkamali-Anaraki, MA Hariri-Ardebili 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021 | 57 | 2021 |
| Memory and computation efficient PCA via very sparse random projections FP Anaraki, S Hughes International Conference on Machine Learning, 1341-1349, 2014 | 57 | 2014 |
| Compressive k-svd FP Anaraki, SM Hughes 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 42 | 2013 |
| Randomized clustered nystrom for large-scale kernel machines F Pourkamali-Anaraki, S Becker, M Wakin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 40 | 2018 |
| Efficient solvers for sparse subspace clustering F Pourkamali-Anaraki, J Folberth, S Becker Signal Processing 172, 107548, 2020 | 39 | 2020 |
| Improved fixed-rank Nyström approximation via QR decomposition: Practical and theoretical aspects F Pourkamali-Anaraki, S Becker Neurocomputing 363, 261-272, 2019 | 33 | 2019 |
| Kernel matrix approximation on class-imbalanced data with an application to scientific simulation P Hajibabaee, F Pourkamali-Anaraki, MA Hariri-Ardebili IEEE Access 9, 83579-83591, 2021 | 30 | 2021 |
| Instrumented health monitoring of an earth dam SM Seyed-Kolbadi, MA Hariri-Ardebili, M Mirtaheri, F Pourkamali-Anaraki Infrastructures 5 (3), 26, 2020 | 26 | 2020 |
| Estimation of the sample covariance matrix from compressive measurements F Pourkamali‐Anaraki IET Signal Processing 10 (9), 1089-1095, 2016 | 26 | 2016 |
| Application of machine learning in polymer additive manufacturing: A review T Nasrin, F Pourkamali‐Anaraki, AM Peterson Journal of Polymer Science 62 (12), 2639-2669, 2024 | 24 | 2024 |
| Efficient dictionary learning via very sparse random projections F Pourkamali-Anaraki, S Becker, SM Hughes 2015 International Conference on Sampling Theory and Applications (SampTA …, 2015 | 23 | 2015 |
| A unified nmpc scheme for mavs navigation with 3d collision avoidance under position uncertainty SS Mansouri, C Kanellakis, B Lindqvist, F Pourkamali-Anaraki, ... IEEE Robotics and Automation Letters 5 (4), 5740-5747, 2020 | 22 | 2020 |
| Scalable spectral clustering with Nyström approximation: Practical and theoretical aspects F Pourkamali-Anaraki IEEE Open Journal of Signal Processing 1, 242-256, 2020 | 17 | 2020 |
| Matrix completion for cost reduction in finite element simulations under hybrid uncertainties MA Hariri-Ardebili, F Pourkamali-Anaraki Applied Mathematical Modelling 69, 164-180, 2019 | 17 | 2019 |
| Neural networks and imbalanced learning for data-driven scientific computing with uncertainties F Pourkamali-Anaraki, MA Hariri-Ardebili IEEE Access 9, 15334-15350, 2021 | 16 | 2021 |
| An automated machine learning engine with inverse analysis for seismic design of dams MA Hariri-Ardebili, F Pourkamali-Anaraki Water 14 (23), 3898, 2022 | 15 | 2022 |
| Kernel Compressive Sensing FP Anaraki, SM Hughes Image Processing (ICIP), 2013 20th IEEE International Conference on, 494-498, 2013 | 14 | 2013 |
M. Amin Hariri-ArdebiliNational Institute of Standards and Technology (NIST)Dirección de correo verificada de nist.gov
