Berkin Bilgic

About me:

I am an Assistant Professor in Radiology at Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School.

I am interested in MRI data acquisition and reconstruction, in particular,

  • Parallel imaging and compressed sensing,
  • Machine Learning,
  • Quantitative parameter mapping, and
  • Diffusion imaging.

Biography:
  • Assistant Professor in Radiology, MGH/Harvard, Jun 2019 -
  • Affiliated Faculty, Health Sciences & Technology, Harvard-MIT, Jun 2018 -
  • Instructor in Radiology, MGH/Harvard, May 2016 - Jun 2019
  • Research Fellow in Radiology, MGH/Harvard, Feb 2013 - May 2016
  • PhD in EECS, MIT, Feb 2010 - Feb 2013
  • SM in EECS, MIT, Sep 2008 - Feb 2010
  • BS in EE, Bogazici University, Sep 2004 - Jun 2008
  • BS in Physics, Bogazici University, Sep 2004 - Jun 2008

News: Software from the upcoming ISMRM'22 conference:
  • Y Arefeen et al: Learning compact latent representations of signal evolution for improved shuffling reconstruction, #0247 Latent shuffling code on Github
  • J Cho et al: Variable Flip, Blip-Up and -Down Undersampling (VUDU) Enables Motion-Robust, Distortion-Free Multi-Shot EPI, #0757 VUDU code on Github
  • J Cho et al: Rapid Quantitative Imaging Using Wave-Encoded Model-Based Deep Learning for Joint Reconstruction, #0435 wave-MoDL code on Github
  • MY Avci et al: Quantifying the uncertainty of neural networks using Monte Carlo dropout for safer and more accurate deep learning based quantitative MRI, #4978 Monte Carlo dropout code on Github
  • A Lin et al: Bayesian sensitivity encoding enables parameter-free, highly accelerated joint multi-contrast reconstruction, #3444 joint Bayesian sensitivity encoding code on Github
  • TH Kim et al: Accelerated MR Parameter Mapping with Scan-specific Unsupervised Networks, #4402 MAPLE code on Github
  • G Varela-Mattatall et al: Rapid CS-Wave MPRAGE acquisition with automated parameter selection, #1604 CS-Wave code on Github
Educational talk from ISMRM'22:
  • Value of multi-contrast techniques (neuro), Wednesday session on Added Value of Sophisticated Multicontrast Techniques recording

  • We continuously seek new research interns at undergraduate, graduate and postdoctoral levels. Please email Berkin for details.


    We gratefully acknowledge our completed or current funding:

    NVIDIA GPU Grant to support machine learning research
    Chinese Scholarship Council (CSC) fellowship: (to Zijing Zhang)
    Office of China Postdoc Council (OCPC) fellowship: (to Zhifeng Chen)
    MIT International Science & Technology Initiatives (MISTI) Grant
    MGH ECOR Formulaic Bridge Funding
    NIH R01 EB028797
    NIH R03 EB031175
    ISMRM Research Exchange Grant Program (to Gabriel Varela-Mattatall)

    Contact Information:

    Berkin

    Building 75
    13th Street
    Charlestown, MA 02129

    E-mail: bbilgic AT mgh.harvard.edu

    Tel: 617-866-8740