| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1774 | 2018 |
| A kernel-independent FMM in general dimensions WB March, B Xiao, S Tharakan, DY Chenhan, G Biros SC'15: Proceedings of the International Conference for High Performance …, 2015 | 34 | 2015 |
| Robust treecode approximation for kernel machines WB March, B Xiao, S Tharakan, CD Yu, G Biros Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 14 | 2015 |
| SIBIA-GlS: Scalable biophysics-based image analysis for glioma segmentation A Mang, S Tharakan, A Gholami, N Nimthani, S Subramanian, J Levitt, ... The multimodal brain tumor image segmentation benchmark (BRATS), MICCAI, 2017 | 10 | 2017 |
| Method for Determining a Spectrum from Time-Varying Data JW Smith, SP Boyd, AE Schoen, SD Tharakan US Patent App. 14/263,947, 2015 | 5 | 2015 |
| Scalable Kernel Methods for Uncertainty Quantification S Tharakan, WB March, G Biros Recent Trends in Computational Engineering-CE2014: Optimization, Uncertainty …, 2015 | 1 | 2015 |
| Using global low-rank kernel matrix approximations in machine learning and uncertainty quantification S Tharakan | | 2020 |
George BirosProfessor, Oden Institute for Computational Engineering and Sciences, The University of Texas atVerified email at acm.org
