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> Machine learning for medical imaging > Compressed sensing theory > Stochastic optimisation for large-scale machine learning

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Personal profile

Research interests

Mohammad received his Ph.D. degree (2012) in Computer and Communication Sciences from the École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His Ph.D. thesis focused on compressed sensing and source separation strategies for multichannel data. He was a CNRS postdoctoral researcher in Applied Mathematics Research Centre (CEREMADE) at Université Paris Dauphine, France, in 2013. He was awarded the Swiss National Science Foundation (SNSF) Fellowship and visited the DSP group at Rice University, Houston TX USA, in 2014. In 2015, he joined the School of Engineering at the University of Edinburgh as an EPSRC Research Associate and held an early career award from the Scottish Research Partnership in Engineering (SRPe) for the project “Accelerating quantitative Magnetic Resonance Imaging acquisition and reconstruction”. Since August 2018, Mohammad joined the University of Bath as an assistant professor (lecturer) in Computer Science.

His research interests include machine learning, signal and image processing, compressed sensing, low-complexity data models, source separation, optimisation algorithms for large-scale machine learning and data science: theoretical and applied to medical imaging and computer vision.

For more information please visit my personal webpage!


Fingerprint Dive into the research topics where Mohammad Golbabaee is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

computer vision Earth & Environmental Sciences
engineering Earth & Environmental Sciences
signal processing Earth & Environmental Sciences
mathematics Earth & Environmental Sciences
image processing Earth & Environmental Sciences
communication Earth & Environmental Sciences
science Earth & Environmental Sciences
machine learning Earth & Environmental Sciences

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Research Output 2015 2020

  • 6 Article
  • 3 Paper
  • 2 Conference article
  • 1 Conference contribution

CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction

Golbabaee, M., Chen, Z., Wiaux, Y. & Davies, M., 8 Oct 2019, (Accepted/In press) In : Inverse Problems.

Research output: Contribution to journalArticle

Open Access

Deep Fully Convolutional Network for MR Fingerprinting

Chen, D., Golbabaee, M., Gómez, P. A., Menzel, M. I. & Davies, M., 10 Jul 2019.

Research output: Contribution to conferencePaper

Open Access

Deep MR Fingerprinting with total-variation and low-rank subspace priors

Golbabaee, M., Pirkl, C. M., Menzel, M. I., Buonincontri, G. & Gómez, P. A., 16 May 2019.

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Geometry of Deep Learning for Magnetic Resonance Fingerprinting

Golbabaee, M., Chen, D., Gómez, P. A., Menzel, M. I. & Davies, M., 1 May 2019, In : IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019, p. 7825-7829 5 p.

Research output: Contribution to journalConference article

Model-based super-resolution reconstruction of T2 maps

Bano, W., Piredda, G. F., Davies, M., Marshall, I., Golbabaee, M., Meuli, R., Kober, T., Thiran, J-P. & Hilbert, T., 13 Sep 2019, In : Magnetic Resonance in Medicine. 83, 3, p. 906-919 14 p.

Research output: Contribution to journalArticle

Open Access