Ukash Nakarmi Source Confirmed

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Assistant Professor

University of Arkansas at Little Rock

faculty

12 h-index 45 pubs 440 cited

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Biography and Research Information

OverviewAI-generated summary

Dr. Ukash Nakarmi's research focuses on developing data-driven solutions using machine learning, computational imaging, and signal processing. His work specifically targets applications within medical imaging, healthcare, and biomedicine. He currently serves as an Assistant Professor at the University of Arkansas, where he also leads the Computational Analytics track within the Data Science Program.

Dr. Nakarmi's publications demonstrate a range of interests within his primary focus areas. Recent work includes investigations into artificial intelligence for remote patient monitoring in heart failure, the application of graph neural networks for analyzing noisy fMRI datasets, and kernel regression for dynamic MRI data. He has also published on topics related to photonics, including frequency-modulated signal generation using semiconductor lasers, and advanced techniques for MRI reconstruction using deep learning methods.

His academic background includes postdoctoral training at Stanford University and a Ph.D. in Electrical Engineering from the University at Buffalo. Dr. Nakarmi has a publication record reflected by an h-index of 12 and over 440 citations. He actively collaborates with researchers across the University of Arkansas system, including colleagues at the University of Arkansas at Fayetteville and the University of Arkansas for Medical Sciences.

Metrics

  • h-index: 12
  • Publications: 45
  • Citations: 440

Selected Publications

  • Efficient Back-Projection Technique for Multiple Objects Detection and 2D Imaging Through Photonics-Based LFM Radar (2025) DOI
  • Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique (2024) DOI
  • Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks (2024) DOI
  • Abstract 4138376: A Machine Learning Approach to Predict Percutaneous Coronary Intervention in Patients with Critical Illness and Signs of Myocardial Injury (2024) DOI
  • Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction (2024) DOI
  • Multi-Radar Interference Mitigation in Photonics-Based Radar With Sliding Window LSTM Recurrent Neural Network (2024) DOI
  • DeepLIR: Attention-Based Approach for Mask-Based Lensless Image Reconstruction (2024) DOI
  • When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging (2023) DOI
  • On Training Model Bias of Deep Learning based Super-resolution Frameworks for Magnetic Resonance Imaging (2023) DOI
  • Multi-Chirp LFM Waveforms Generation With Reconfigurable Chirp Rates Using Optical Injection in a Semiconductor Laser (2023) DOI
  • BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI Dataset (2022) DOI
  • Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications (2022) DOI
  • Photonically Generated Frequency Hopped Linear Frequency Modulated Signal Using a DFB Laser (2022) DOI
  • Kernel Regression Imputation in Manifolds Via Bi-Linear Modeling: The Dynamic-MRI Case (2022) DOI
  • Kernel Regression Imputation in Manifolds via Bi-Linear Modeling: The Dynamic-MRI Case (2021) DOI

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