BISPL- BioImaging, Signal Processing & Learning Lab @ KAIST AI
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Latest Research Highlight
Denoising MCMC for Accelerating Diffusion-Based Generative Models

Kim., et al. International Conference on Machine Learning (ICML), 2023.

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Two papers accepted to ICML 2023!

4/28/2023

 
Congraturations! The following two papers fro BISPL are accepted for ICML 2023.
  • Beomsu Kim, and Jong Chul Ye, "Denoising MCMC for Accelerating Diffusion-Based Generative Models", International Conference on Machine Learning (ICML),  oral presentation, 2023 
  • Lee, Sangyun, Beomsu Kim, and Jong Chul Ye. "Minimizing Trajectory Curvature of ODE-based Generative Models." International Conference on Machine Learning (ICML), (2023).

Congraturations!

3/9/2023

 
BISPL has a few things to congratulate!
  • Our PhD student Hyungjin Chung received Gold Award in Signal Processing Area at 2023 ​Humantech Thesis Award.
  • BISPL alumni Dr. Yoseob Han has become an ​Assistant Professor at the School of Electronic Engineering at Soongsil Univeristy.   http://infocom.ssu.ac.kr/kor/undergraduate/0202.php.    So far BISPL has produced 7 assistant professors from our graduate students.

Three papers accepted to CVPR 2023!

3/9/2023

 
The following three papers from BISPL got accepted to CVPR 2023. Congraturations!
  • Geon Yeong Park, Sangmin Lee, Sang Wan Lee, Jong Chul Ye, "Training debiased subnetworks with contrastive weight pruning", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  • Hyungjin Chung, Jeongsol Kim, Sehui Kim, Jong Chul Ye,"Parallel Diffusion Models of Operator and Image for Blind Inverse Problems", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  • Hyungjin Chung, Dohoon Ryu, Michael T. Mccann, Marc L. Klasky, Jong Chul Ye,"Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.​​

Three papers accepted to ICLR 2023!

1/21/2023

 
Congraturation! The following three papers from BISPL are accepted to ICLR 2023. One of them is for spot-light presentation.
​
  1. Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, Jong Chul Ye, "Diffusion Posterior Sampling for General Noisy Inverse Problems", International Conference on Learning Representations (ICLR), 2023  Spotlight (notable-top-25%) Presentation.
  2. Boah Kim, Yujin Oh, Jong Chul Ye,"Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation", International Conference on Learning Representations (ICLR), 2023 
  3. Gihyun Kwon, Jong Chul Ye, "Diffusion-based Image Translation using disentangled style and content representation",  International Conference on Learning Representations (ICLR), 2023 

Recent Paper in Nature Machine Intelligence

11/4/2022

 
Lee et al, "Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data",  Nature Machine Intelligence (in press), 2022.

​This year we have  4 Nature Papers (2 Nature Comm,  1 Nature Machine Intelligence, 1 Nature Review Cardiology).
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    BISPL@KAIST AI

    BISPL is a group of people at KAIST who are eager to dedicate their time and effort to investigate the beauty of bio- and medical imaging with the help of mathematics, machine learning, and physics.
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 bio-medical 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 bio-medical 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. 
CONTACT US
Bio Imaging. Signal Processing & Learning
Graduate School of AI
KAIST
291 Daehak-ro, Yuseong-gu
Daejeon 305-701, Korea

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