Nima Yaghoobi

Nima is a Ph.D. student in Electrical and Computer Engineering at the University of Virginia, advised by Prof. Mathews Jacob. He is a member of the Computational Biomedical Imaging Group (CBIG). His research focuses on developing deep learning and machine learning algorithms for advanced MRI reconstruction and denoising, particularly for dynamic cardiac and brain imaging. He is passionate about leveraging data-driven approaches to improve image quality and accelerate clinical workflows in medical imaging.
Nima's Picture

About Me

I am a Ph.D. student in Electrical and Computer Engineering at the University of Virginia and a member of the Computational Biomedical Imaging Group (CBIG), led by Prof. Mathews Jacob. My research focuses on the development of advanced machine learning and deep learning algorithms for solving inverse problems in medical imaging, particularly Magnetic Resonance Imaging (MRI).

Before starting my Ph.D., I earned my Master’s degree in Electrical Engineering – Digital Electronics from the University of Guilan, Iran, where I worked on improving the signal-to-noise ratio in multi-coil MR data using statistical models of noise. During my Bachelor’s studies in Electrical Engineering at the same university, I developed a strong foundation in electronics and control systems and completed a thesis on simulating humanoid robot motion using mathematical modeling.

I work on both dynamic cardiac and brain MRI applications, aiming to improve image quality, reduce scan time, and enhance diagnostic reliability through data-driven and model-based reconstruction techniques. My projects integrate energy-based models, score-based regularizers, and optimization methods to enable robust and efficient reconstruction from undersampled and noisy data.

 

Research Interests

  • Image Reconstruction
  • Magnetic Resonance Imaging
  • Image Processing
  • Machine Learning
  • Deep Learning

 

Education

  • Ph.D. in Electrical and Computer Engineering, University of Virginia, USA
    Advisor: Prof. Mathews Jacob
    Focused on MRI reconstruction and denoising using machine learning and deep learning models.
  • M.Sc. in Electrical Engineering – Digital Electronics, University of Guilan, Iran
    GPA: 3.81/4
    Conducted research on improving signal-to-noise ratio in multi-coil MR imaging using statistical noise modeling (nc-χ distribution).
  • B.Sc. in Electrical Engineering, University of Guilan, Iran
    GPA: 3.60/4
    Completed foundational training in electronics and control systems, with a final project on simulating humanoid robot motion using mathematical modeling.

 

Honors and Awards

  • Research Scholarship awarded for publication on multi-coil MR image denoising (2019)
  • Top Researcher in the Department of Electrical Engineering, University of Guilan (2018)
  • 1st in Class (GPA 3.81/4) among Master’s graduates in Digital Electronics (2018)
  • 2nd in Class (GPA 3.60/4) among Bachelor’s graduates in Electrical Engineering (2014)
  • Finalist in the 19th Student Scientific Olympiad in Electrical Engineering (Iran), qualified through multiple provinces (2014)

 

Publications

  • Accelerating 3D Radial MPnRAGE Using a Self-supervised Deep Factor Model, Y. Chen, N. Yaghoobi et al., Magnetic Resonance in Medicine, 2025
  • Fast Multi-contrast MRI Using Joint Multiscale Energy Model, N. Yaghoobi et al., arXiv:2501.06595, 2025
  • De-noising of 3D Multiple-coil MR Images Using Modified LMMSE Estimator, N. Yaghoobi, R. Hasanzadeh, Magnetic Resonance Imaging, 2018