BISPL @ KAIST AI - BioImaging, Signal Processing, & machine Learning lab.
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Jong Chul Ye
 IEEE Fellow  "for contributions to signal processing and machine learning for bio-medical imaging​"
 KAIST Endowed Chair Professor (KAIST 지정 석좌교수) / Professor
 Kim Jaechul Graduate School of AI
 N5 Building,  Room 2221
 291 Daehak-ro, Yuseong-gu, Daejeon 34141
 Korea Advanced Institute of Science & Technology (KAIST)
 E-mail: [email protected]
 Tel: +82-42-350-4320
 Fax : +82-42-350-4310
Google Scholar Citation 

Education
  • Postdoctoral Researcher, Coordinate Science Lab., Univ. of Illinois at Urbana-Champaign, 1999-200
           Advisors: Yoram Bresler, and Pierre Moulin

  • Ph.D. Electrical and Computer Engineering, Purdue University, May 1999
          Dissertation: Estimation and reconstruction for the optical diffusion nonlinear inverse problem
           Advisor: Kevin Webb, and Charles Bouman

  • M.S. Control and Instrumentation Engineering (now, ECE), Seoul National University, Korea, Feb. 1995
          Dissertation: Model-based video compression technology using segmentation
          Advisor: Sangwook Lee

  • B.S. Control and Instrumentation Engineering (now, ECE), Seoul National University, Korea, Feb. 1993
          Advisor: Sang Myung Koh


Work Experiences
 -     Jan. 2022 - current           Full Professor (tenured), Graduate School of AI, KAIST
​·      March 2016                       KAIST Endowed Chair Professor
·       Aug. 2004 -  Dec. 2021    Assistant, Associate, and Full Professor (tenured), Department of Bio and Brain Engineering, KAIST
·       Mar. 2017 -  current         Adjunct Professor, Department of Mathematical Sciences, KAIST
·       July  2014  -  Aug. 2015   Interim Department Head,  Dept. of Bio and Brain Engineering, KAIST
·       Mar. 2007  -  Marh 2013  Adjunct Professor, Department of Electrical Engineering, KAIST, Daejon, Korea
·       2003.     -     2004.             Senior Researcher, X-ray CT Technology Group, GE Global Research Center, New York
·       2001.     -     2003.             Senior Member Research Staff, Philips Research Center, Briarcliff Manor, New York
 

Leadership/ Editorial Positions
  • IEEE Fellow,        effective 1 January 2020, with the following citation 
                                       “for contributions to signal processing and machine learning for bio-medical imaging”
  • Distinguished Lecturer,  IEEE EMBS,  2020-2021.
  • General Chair,      IEEE Symp. On Biomedical Imaging (ISBI), Iowa City, 2020.
  • Program Chair,     IEEE Conf.  Acoustics, Speech and Signal Processing (ICASSP), Seoul, 2024
  • Chair,  IEEE Technical Committee on Computational Imaging (TC CI), 2020-2021.12
  • Vice President,      Korean Society for Artificial Intelligence in Medicine (KoSAIM), 2019-
  • Senior Editor,  EEE Signal Processing Magazine, Mar. 2018 – current
  • Associate Editor, IEEE Trans. Medical Imaging, May 2018 - current
  • Section Editor,  BMC Biomedical Engineering, July 2018  - current
  • International Advisory Board,  Physics in Medicine and Biology, Jan. 2017  -  Dec. 2018 
  • Associate Editor,  IEEE Transactions on Computational Imaging, Sept. 2014 -  Dec. 2018 : 
  • Guest Editor for IEEE Journal of Selected Topics in Signal Processing Special Issue on “Domain Enriched Learning for Medical Imaging”, Aug. 2019 – current
  • Guest Editor for  IEEE Signal Processing Maganize for Special Issue on “Comptuational MRI: Compressed Sensing and Beyond”, Dec. 2018 – current 
  • Guest Editor  for IEEE Trans. Medical Imaging for Special Issue on “Machine Learning for Image Reconstruction”, Mar. 2017 – May 2018
  • Associate Editor, IEEE Transactions on Image Processing, Jan. 2013   -  Dec. 2015
  • Editorial Board Member,  Magnetic Resonance in Medicine, Jan. 2015   -  Dec. 2017 

Professional Activities
  • Elected Member of IEEE Signal Processing Society (SPS) TC   Computational Imaging (CI), 8/2015-current
  • Elected member of IEEE Signal Processing Society (SPS) TC Bioimaging and Signal Processing (BISP), 1/2013–12/2018
  • IEEE Engineering in Biology &Medicine Society (EMBS) TC Biomedical Imaging and Image Processing), 7/2012–current

Research Interests
  • Machine learning, deep learning
  • Computer vision, generative models
  • Computational Imaging
  •  MRI signal processing (parallel imaging, time-sequential sampling, k-t sampling etc) 
  • ​CT/PET reconstruction algorithm 
  • Super-resolution microscopy
  • Ultrasound imaging, inverse scattering problems
  •  Statistical signal processing, inverse problem 


Honors and Awards
  • Fellow of IEEE (Jan. 2020) with the citation “for contributions to signal processing and machine learning for bio-medical imaging”
  • IEEE EMBS Distinguished Lecturer, Jan. 2020 -Dec. 2021
  • KumGok Technical Achievement Award, Korea Society for Industrial and Applied Math (KSIAM), 2022
  • Gold Medal for Technical Achievement,  Korean Society for Magnetic Resonance Imaging (KSMRM), 2021
  • 1st place winner of Reconstruction Challenge, ISMRM Workshop on Data Sampling and Image Reconstruction (2009)
  • KAIST Research Excellence Award (Feb. 2012)
  • Beckman Senior Fellowship Award, Univ. of Illinois at Urbana-Champaign (Feb. 2012 - Jan. 2013)
  • Best student papers (1st, 2nd) from IEEE International Symp. on Biomedical Imaging (ISBI) (2013,  2016)      
  • 2nd Place Award, AAPM (American Association of Physicist in Medicine) Low-Dose CT Grand Challenge (2016)
  • 3rd Place Award,  CVPR NTIRE (New Trends in Image Restoration and Enhancement workshop) on Super-Resolution Imaging Challenge    (2017)


Plenary, Keynote, and Tutorial Talks
  • Geometry of Deep Learning for Inverse Problems, Keynote talk,  ICCV (IEEE Conf. on Computer Vision) Learning Computational Imaging (LCI) Workshop, Nov.2, 2019, Seoul, Korea
  • Geometrical Understanding of CNN for Biomedical Image Reconstruction, Keynote talk,  the 2nd PRIME (PRedictive Intelligence in Medicine) Workshop @  MICCAI, Oct 13th, Shenzhen, China
  • Geometry of Convolutional Neural Networks for Computational Imaging, Keynote talk,  IMA (Institute for  Mathematics and Its Applications) Special Workshop on Computational Imaging, Oct, Minneapolis,  16th, 2019.
  • Understanding Geometry of Encoder-Decoder CNNs for Inverse Problems,  Plenary Talk, Applied Inverse Problems (AIP) conference, Grenoble, July 11th, 2019
  • Machine Learning in Medical Imaging, Plenary Talk, The 27th Annual Meeting of ISMRM, Montreal, Canada, May 14th, 2019
  • Low-Rank plus Sparse Reconstruction, Tutorial Talk @ Sunday Education Session​, ​The 27th Annual Meeting of ISMRM, May 12th, 2019, Montreal, Canada
  • Deep Learning for Biomedical Image Reconstruction, Tutorial Talk, IEEE Symp. on Biomedical Imaging (ISBI), April 11th, 2019, Venice, Italy
  • Overview of Machine Learning Methods for Reconstruction of Imaging Data, Keynote Talk, ​ISMRM Workshop on Machine Learning, Part II, Oct 26, 2018, Washington DC, USA
  • Deep Convolutional Framelets: A general deep learning framework for inverse problems, Keynote Talk, MLMIR - Machine Learning for Medical Image Reconstruction, MICCAI Workshop, Sept. 16th, 2018, Granada, Spain
  • Sparse and Deep Learning Approaches for Biomedical Image Reconstruction, Extensive tutorial lecture, the 13th IEEE EMBS International Summer School on Biomedical Imaging, June 14-21, 2018 , Saint-Jacut de la Mer, France
  • Deep Learning for CT Reconstruction: From Concept to Practices, Refresher Course Tutorial, CT Meeting 2018, The Fifth International Conference on Image Formation in X-Ray Computed Tomography, May 20-23, 2018, Salt Lake City, USA
  • Deep Learning for CT Reconstruction, Keynote Talk, SPIE Medical Imaging 2018, Feb, 24, 2018, Houston, USA
  • Continuous domain sparse recovery of biomedical image data using structured low-rank approaches, Tutorial talk, IEEE Symposium on Biomedical Imaging (ISBI), April 18, 2017, Melbourne, Australia

Global Media Interview
  • AuntMinnie.com 5/15/2018, Interview after the plenary talk at ISMRM 2019
    • https://www.auntminnie.com/index.aspx?sec=rca&sub=ismr_2019&pag=dis&ItemID=125484

  • ISMRM’s MR Pulse Blog
    • QA Yoseob Han and Jong Chul Ye, 8/23/2019.
      • https://blog.ismrm.org/2019/08/23/qa-yoseob-han-and-jong-chul-ye/
    • QA Dongwook Lee and Jong Chul Ye, 12/8/2016.
      • https://www.ismrm.org/qa-with-dongwook-lee-and-jong-chul-ye/



ABOUT US
Our research activities are primarily focused on the signal processing and machine learning  for high-resolution high-sensitivity image reconstruction from real world bomedical imaging systems. Such problems pose interesting challenges that often lead to investigations of fundamental problems in various branches of physics, mathematics, signal processing, biology, and medicine. While most of the biomedical imaging researchers are interested in addressing this problem using off-the-self tools from signal processing, machine learning, statistics, and optimization and combining their domain-specific knowledge, our approaches are unique in the sense that I believe that actual  imaging applications are a source of endless inspiration for new mathematical theories and we are eager to solve both specific applications and application-inspired fundamental theoretical problems. 
Our location
Graduate School of AI
KAIST
108  Taebong-ro, Seocho-gu,  Seoul,  06764
Republic of Korea


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