Curriculum Vitae
 Picture
 
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
 Korea Advanced Institute of Science & Technology (KAIST)
 E-mail: jong.ye@kaist.ac.kr
 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
  • 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