Joint wideband spectrum sensing in frequency overlapping cognitive radio networks using distributed compressive sensing. - Ukash Nakarmi; Nazanin Rahnavard (MILCOM, 2011)

BCS: Compressive sensing for binary sparse signals. - Ukash Nakarmi; Nazanin Rahnavard (MILCOM, 2012)

Dynamic magnetic resonance imaging using compressed sensing with self-learned nonlinear dictionary (NL-D). - Ukash Nakarmi; Yanhua Wang; Jingyuan Lyu; Leslie Ying (ISBI, 2015)

Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding. - Ukash Nakarmi; Yihang Zhou; Jingyuan Lyu; Konstantinos Slavakis; Leslie Ying (ISBI, 2016)

Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery. - Konstantinos Slavakis; Gaurav N. Shetty; Abhishek Bose; Ukash Nakarmi; Leslie Ying (CAMSAP, 2017)

M-MRI: A manifold-based framework to highly accelerated dynamic magnetic resonance imaging. - Ukash Nakarmi; Konstantinos Slavakis; Jingyuan Lyu; Leslie Ying (ISBI, 2017)

A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI. - Ukash Nakarmi; Yanhua Wang; Jingyuan Lyu; Dong Liang 001; Leslie Ying (IEEE Trans. Medical Imaging, 2017)

MLS: Joint manifold-learning and sparsity-aware framework for highly accelerated dynamic magnetic resonance imaging. - Ukash Nakarmi; Konstantinos Slavakis; Leslie Ying (ISBI, 2018)

Kernel and Manifold Framework for Magnetic Resonance Imaging. - Ukash Nakarmi (2018)

KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction. - Jingyuan Lyu; Ukash Nakarmi; Dong Liang 001; Jinhua Sheng; Leslie Ying (IEEE Trans. Medical Imaging, 2019)

Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case. - Gaurav N. Shetty; Konstantinos Slavakis; Ukash Nakarmi; Gesualdo Scutari; Leslie Ying (EUSIPCO, 2020)

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges. - Jeffrey J. Ma; Ukash Nakarmi; Cedric Yue Sik Kin; Christopher M. Sandino; Joseph Y. Cheng; Ali B. Syed; Peter Wei; John M. Pauly; Shreyas S. Vasanawala (ISBI, 2020)

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging. - Ukash Nakarmi; Joseph Y. Cheng; Edgar P. Rios; Morteza Mardani; John M. Pauly; Leslie Ying; Shreyas S. Vasanawala (ISBI, 2020)

Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images. - Edgar A. Rios Piedra; Morteza Mardani; Frank Ong; Ukash Nakarmi; Joseph Y. Cheng; Shreyas Vasanawala (CoRR, 2020)

Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method. - Chaoyi Zhang; Tanzil Mahmud Arefin; Ukash Nakarmi; Choong-Heon Lee; Hongyu Li; Dong Liang 001; Jiangyang Zhang; Leslie Ying (NeuroImage, 2020)

Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery. - Gaurav N. Shetty; Konstantinos Slavakis; Abhishek Bose; Ukash Nakarmi; Gesualdo Scutari; Leslie Ying (IEEE Trans. Medical Imaging, 2020)

Parallel MRI Reconstruction Using Broad Learning System. - Yuchou Chang; Ukash Nakarmi (EMBC, 2021)

BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI Dataset. - Quan Mai; Ukash Nakarmi; Miaoqing Huang (BIBM, 2022)

Kernel Regression Imputation in Manifolds Via Bi-Linear Modeling: The Dynamic-MRI Case. - Konstantinos Slavakis; Gaurav N. Shetty; Loris Cannelli; Gesualdo Scutari; Ukash Nakarmi; Leslie Ying (IEEE Trans. Computational Imaging, 2022)

On Training Model Bias of Deep Learning based Super-resolution Frameworks for Magnetic Resonance Imaging. - Mamata Shrestha; Nian Wang; Ukash Nakarmi (BHI, 2023)

When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging. - Ibsa Jalata; Ukash Nakarmi (ISVC (1), 2023)

Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique. - Ibsa Jalata; Ukash Nakarmi (BIBM, 2024)

Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks. - Ibsa Jalata; Ukash Nakarmi (EMBC, 2024)

DeepLIR: Attention-Based Approach for Mask-Based Lensless Image Reconstruction. - Arpan Poudel; Ukash Nakarmi (WACV (Workshops), 2024)

Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction. - Ibsa Jalata; Reeshad Khan; Ukash Nakarmi (IEEE Access, 2024)

Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials. - Shakil Rafi; Joshua Lee Padgett; Ukash Nakarmi (CoRR, 2024)

Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising. - Reeshad Khan; John Gauch; Ukash Nakarmi (CoRR, 2024)

From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising. - Reeshad Khan; Ukash Nakarmi; John M. Gauch (VISIGRAPP (3): VISAPP, 2025)