Tran Minh Quan

     Ms-PhD. Student
     E-mail: quantm (at)




Research Interests

  • GPU computing
  • Compressive Sensing MRI

Compressive Sensing has drawn many attractions from researchers in diverse fields not only in signal processing area but also bioclinical department. In Magnetic Resonance Imaging (MRI), it provides a mathematical description to quickly perform the scanning process but still preserve the good quality of the images. Taking into account the large-scaled and high-dimensional data keep growing up from the scanner, we focus on developing a multi-GPU heterogeneous system that can speed up the reconstruction process. With the modified Split-Bregman method, we propose the new CSMRI scheme which is running fast, scalable, and arise the images as good as conventional diagnosis.

Research topic: Compressive Sensing MRI on a multi-GPU system including
  • Fast Inverse Problems
  • Fast Wavelet Transform
  • High-performance Computing on Heterogeneous Parallel Systems


  • B.S. in Electrical Engineering, KAIST, Daejeon, Korea (2012)

Work Experience

  • Smart Sensor Architecture Laboratory, National Nano Fabrication Center (Daejeon, Korea), research assistant, 2011.
  • Photonic Energy and Signal Processing Laboratory (Daejeon, Korea),  winter intern, 2011.
  • L&Y Vision Technology Corporation (Daejeon, Korea),  summer intern, 2011.
  • VLSI Systems Laboratory, National Nano Fabrication Center (Daejeon, Korea), summer intern, 2010.


[bibtex file=quan_2018_refinegan.bib]

[bibtex file=quan_jun_haejin_vis_2017.bib]

[bibtex file=hildebrand_nature_2017.bib]

[bibtex file=quan_compressed3d_2016.bib]


[bibtex file=quan_fast_2016.bib]


[bibtex file=quan_compressed_2016.bib]


[bibtex file=quan_multi_2015.bib]


[bibtex file=choi_vivaldi_2014.bib]


[bibtex file=quan_fast_2013.bib]



  • Various optimization strategies for implementing fast discrete wavelet transforms on GPUs
    Tran Minh Quan, Won-Ki Jeong
    NVIDIA GPU Technology Conference Korea (GTCx)  at Seoul, South Korea, 2016
  • S3308 – Fast Compressive Sensing MRI Reconstruction on a Multi-GPU System
    Tran Minh Quan, Won-Ki Jeong
    NVIDIA GPU Technology Conference (GTC) at San Jose, California, USA, 2013

Honor / Award

  • 2016 Aug: NAVER PhD Fellowship awarded.
  • 2016 Apr: MICCAI Student Travel Grant awarded.
  • 2015 Apr: MICCAI Student Travel Grant awarded.
  • 2014 Jun: IEEE SPS Student Travel Grant awarded.
  • 2013 Aug: Statement of Accomplishment (Distinction)
    of Interactive 3D Graphics,
    by Eric Haines, Senior Principal Engineer, Autodesk.
  • 2013 Jul: Statement of Accomplishment (Distinction)
    of Introduction to Parallel Programming: Using CUDA to Harness the Power of GPUs,
    by David Luebke, PhD., Senior Director of Research, Visual Computing, NVIDIA
    and John Owens, PhD., Professor of Electrical and Computer Eng., UC Davis.
  • 2013 Mar: Statement of Accomplishment (Distinction)
    of Image and Video Processing: From Mars to Hollywood with a stop at the hospital,
    by G. Sapiro, PhD., Professor of Electrical and Computer Eng., Duke University.
  • 2013 Feb: Statement of Accomplishment (Distinction)
    of Heterogeneous Parallel  Programming,
    by Wen-Mei Hwu, PhD., Professor of College of Eng., University of Illinois.
  • 2012 Feb:  Full Graduate Scholarship from UNIST
  • 2008 Feb:  Full Undergraduate Scholarship from KAIST
  • 2005 Jul: Campaign Medal in Australian National Chemistry Quiz – Award of Excellence,
    by The Royal Australian Chemical Institute.

Teaching Assistant

Spring 2014 Heterogeneous Parallel Programming, Massive Open Online Course, Coursera
Fall       2013 ECE519 – Massively Parallel Programming, Graduate Course, UNIST
Spring 2013 ITP107 – Engineering Programming 1, Undergraduate Course, UNIST
Fall       2012 CSE231 – Data Structure, Undergraduate Course, UNIST
Spring 2012 CSE431 – Introduction to Computer Graphics, Undergraduate Course, UNIST