Ilyes Benslimane


Ph.D. student




Ilyes holds a Master’s degree in Medical Physics from Columbia University and a Bachelor’s from Loyola University (New Orleans). He is currently a PhD student in the Medical Physics program at UB. Ilyes has developed a post-processing tool, BIOPHYSICSS-DL, that utilizes machine learning to determine and test biophysical models of quantitative MRI metrics. His innovative approach has the potential to provide further insight into the causes and progression of neurodegenerative diseases such as Multiple Sclerosis.Upon graduation, Ilyes hopes to pursue clinical work as a medical physicist, as well as continuing his research. 

In his free time, Ilyes enjoys travelling, writing science-fiction, creating tabletop games, and reading up on mythology. These activities provide him with a creative outlet to help him refresh outside of the lab.

Self-calibrating χ-separation using single-subject (N=1) physics-constrained deep learning.


Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F


15th Annual Buffalo Neuroscience Research Day, Buffalo, NY, 2022


Self-calibration, histological validation, and an improved signal model for χ-separation using single-subject (N=1) physics-constrained deep learning.


Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F


Joint Workshop on MR phase, magnetic susceptibility and electrical properties mapping, Lucca, Italy, 2022


Improved self-calibrated signal model for χ-separation using single-subject (N=1) physics-constrained deep learning with histological validation.


Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F


Artificial Intelligence in Clinical Imaging (AICI) Forum, Graz, Austria, 2022


Beyond qMRI: Biological tissue properties forom single-subject unsupervised deep learning with theoretical signal constraints


Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F


Proc Intl Soc Mag Reson Med, 2022, p. 370


Unsupervised physics-informed deep learning (N=1) for solving inverse qMRI problems–Relaxometry and field mapping from multi-echo data.


Benslimane I, Jochmann T, Zivadinov R, Schweser F


Proc Intl Soc Mag Reson Med, 2021, p. 330