| Chook--A comprehensive suite for generating binary optimization problems with planted solutions D Perera, I Akpabio, F Hamze, S Mandra, N Rose, M Aramon, ... arXiv preprint arXiv:2005.14344, 2020 | 18 | 2020 |
| Uncertainty quantification of machine learning models: on conformal prediction II Akpabio, SA Savari Journal of Micro/Nanopatterning, Materials, and Metrology 20 (4), 041206-041206, 2021 | 6 | 2021 |
| On an application of denoising to the uncertainty quantification of line edge roughness estimation II Akpabio, SA Savari 2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC …, 2022 | 2 | 2022 |
| Uncertainty Quantification in Line Edge Roughness Estimation Using Conformal Prediction II Akpabio | 1 | 2022 |
| On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing II Akpabio, SA Savari arXiv preprint arXiv:2203.03150, 2022 | 1 | 2022 |
| 2023 Index IEEE Transactions on Semiconductor Manufacturing Vol. 36 SS Agashe, I Ahsan, A Akcay, II Akpabio, A Albeshri, C Anthony, ... IEEE Transactions on Semiconductor Manufacturing 36 (4), 2023 | | 2023 |
| On Uses of Noise Analysis for the Uncertainty Quantification of Line Edge Roughness Estimation II Akpabio, SA Savari IEEE Transactions on Semiconductor Manufacturing 36 (3), 319-326, 2023 | | 2023 |
| Joint Source-channel Coding Using Machine Learning Techniques I Akpabio | | 2019 |
