World's Best Scientists 2026 revealed!
Award Badge
Computer Science
Korea
2026

D-Index & Metrics

Computer Science

D-Index
70
Citations
22912
World Ranking
1853
National Ranking
8

Research.com Recognitions

  • 2026 - Research.com Computer Science in Korea Leader Award
  • 2025 - Research.com Computer Science in Korea Leader Award
  • 2022 - Research.com Computer Science in Korea Leader Award
  • 2020 - IEEE Fellow For contributions to signal processing and machine learning for bio-medical imaging

Overview

Jong Chul Ye is affiliated with the Korea Advanced Institute of Science and Technology in South Korea. Their research spans multiple fields, primarily in computer science and medicine, with extensive work in various subfields.

The main fields of study include:

  • Computer Science
  • Medicine

Within these fields, their research is especially concentrated on the following subfields:

  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Biomedical Engineering
  • Biophysics

Their work covers a range of topics related to imaging and machine learning, including:

  • Generative Adversarial Networks and Image Synthesis
  • Image and Signal Denoising Methods
  • Medical Imaging Techniques and Applications
  • Advanced Image Processing Techniques
  • Radiomics and Machine Learning in Medical Imaging
  • Photoacoustic and Ultrasonic Imaging
  • Advanced MRI Techniques and Applications

Jong Chul Ye has published in numerous venues. The most frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • IEEE Signal Processing Magazine
  • IEEE Transactions on Computational Imaging

Some recent significant research papers are:

  • Deep learning for tomographic image reconstruction, 2020, Nature Machine Intelligence
  • DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Score-based diffusion models for accelerated MRI, 2022, Medical Image Analysis
  • CycleMorph: Cycle consistent unsupervised deformable image registration, 2021, Medical Image Analysis
  • CLIPstyler: Image Style Transfer with a Single Text Condition, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors collaborating with Jong Chul Ye include:

  • Hyungjin Chung
  • Sang Joon Park
  • Yujin Oh
  • Gyutaek Oh
  • Gihyun Kwon

Jong Chul Ye has contributed to book publications with these titles:

  • Geometry of Deep Learning, published by Springer Nature in 2022
  • Machine Learning for Medical Image Reconstruction, published by Springer Science+Business Media in 2020

They have been recognized as an IEEE Fellow since 2020, awarded for contributions in signal processing and machine learning for bio-medical imaging.

Best Publications

  • NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

    Radu Timofte;Eirikur Agustsson;Luc Van Gool;Ming-Hsuan Yang

  • NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy.

    Jong Chul Ye;Sungho Tak;Kwang Eun Jang;Jinwook Jung

  • A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

    Eunhee Kang;Junhong Min;Jong Chul Ye

  • k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI.

    Hong Jung;Kyunghyun Sung;Krishna S. Nayak;Eung Yeop Kim

  • Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

    Yujin Oh;Sangjoon Park;Jong Chul Ye

  • Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT

    Yoseob Han;Jong Chul Ye

  • Deep learning for tomographic image reconstruction

    Ge Wang;Jong Chul Ye;Bruno De Man

  • Statistical analysis of fNIRS data: a comprehensive review.

    Sungho Tak;Jong Chul Ye

  • Image Reconstruction is a New Frontier of Machine Learning

    Ge Wang;Jong Chu Ye;Klaus Mueller;Jeffrey A. Fessler

  • Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning

    Saiprasad Ravishankar;Jong Chul Ye;Jeffrey A. Fessler

  • Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

    Eunhee Kang;Won Chang;Jaejun Yoo;Jong Chul Ye

  • Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems

    Jong Chul Ye;Yoseob Han;Eunju Cha

  • ${k}$ -Space Deep Learning for Accelerated MRI

    Yoseo Han;Leonard Sunwoo;Jong Chul Ye

  • Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks

    Dongwook Lee;Jaejun Yoo;Sungho Tak;Jong Chul Ye

  • Improved k t BLAST and k t SENSE using FOCUSS

    Hong Jung;Jong Chul Ye;Eung Yeop Kim

  • Score-based diffusion models for accelerated MRI

    Hyungjin Chung;Jong chul Ye

  • Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing

    Jong Min Kim;Ok Kyun Lee;Jong Chul Ye

  • Deep learning with domain adaptation for accelerated projection-reconstruction MR.

    Yo Seob Han;Jaejun Yoo;Hak Hee Kim;Hee Jung Shin

  • Wavelet minimum description length detrending for near-infrared spectroscopy.

    Kwang Eun Jang;Sungho Tak;Jinwook Jung;Jaeduck Jang

  • CycleMorph: Cycle consistent unsupervised deformable image registration.

    Boah Kim;Dong Hwan Kim;Seong Ho Park;Jieun Kim

  • Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography.

    JooWon Lim;Kyeo Reh Lee;Kyong Hwan Jin;Seungwoo Shin

  • Geometric GAN

    Jae Hyun Lim;Jong Chul Ye

  • A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

    Kyong Hwan Jin;Dongwook Lee;Jong Chul Ye

Frequent Co-Authors

Yoram Bresler
Yoram Bresler University of Illinois at Urbana-Champaign
Pierre Moulin
Pierre Moulin University of Illinois at Urbana-Champaign
Charles A. Bouman
Charles A. Bouman Purdue University West Lafayette
Michael Unser
Michael Unser École Polytechnique Fédérale de Lausanne
Ki-Hun Jeong
Ki-Hun Jeong Korea Advanced Institute of Science and Technology
Radu Timofte
Radu Timofte University of Wurzburg
Daniel Kim
Daniel Kim Northeastern University
Kiran Challapali
Kiran Challapali Philips (Finland)
Habib Ammari
Habib Ammari ETH Zurich
Wangmeng Zuo
Wangmeng Zuo Harbin Institute of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science can open doors to many rewarding online degrees and career paths in the USA. Students interested in cybersecurity, for example, may consider earning a master's degree in cybersecurity online. This area of study is vital for protecting digital infrastructure and offers growing job opportunities.

Those leaning toward project management or engineering may look into the best 2 year construction management degree online. Such programs equip graduates for roles that blend technology with the practicalities of managing large-scale builds.

Interested in law, security, or public administration? Consider affordable online criminal justice programs, which can lead to impactful careers in law enforcement, forensics, or corrections.

Finally, if your passion is for numbers and business, it's important to understand the cost of accounting degree options available online. This field offers flexible work avenues and is in demand across multiple industries.

Best Scientists Citing Jong Chul Ye

Trending Scientists