User:Kangjoo Lee

Contact Info



 * Kangjoo Lee (이강주, 李江珠)
 * CMS B/D 402, KAIST
 * 335 Gwahak-no, Yuseong-gu, Daejeon, 305-701, Republic of Korea
 * E-mail : leekangjoo15@gmail.com
 * Tel : +82-42-350-4360
 * Fax : +82-42-350-4310

I work in the Bio Imaging and Signal Processing Lab at Korea Advanced Institute of Science and Technology (KAIST).

My curriculum vitae can be downloaded here. : [[Media:Kangjoo Lee_CV.pdf]]

Research Interests

 * 1) Neuroimaging
 * 2) Computational neural-network modeling
 * 3) Tumor-imaging Techniques
 * 4) Radiolocal Science for Biomedical Applications


 * My current research is on building statistical analysis of multi-level general linear model for functional brain imaging based on compressed sensing theory. In the field of human functional neuroimaing, I am searching for novel imaging and analysis techniques of functional MRI (fMRI), near-infrared spectroscopy (NIRS) and etc, for the advanced understanding of brain function in neurological and cognitive psychology studies as well as brain diseases such as dementia. Moreover, I’m one of the two pioneer developers of a general-purpose functional MRI analysis toolbox, based on sparse generalized linear model with minimum description length criterion and statistical activation detection.


 * Computational modeling of neural network, brain complex network studies as well as functional connectivity including default-mode network, are also my main interests, which aims to identify the human network behavior in that the regional blobs of neurons can be integrated to function by combination of their intrinsic job and interactions. Functional neuro-imaging techniques such as fMRI, electroencephalography (EEG), magnetoencephalography (MEG), multielectrode array (MEA), and diffusion water fMRI as well as structural brain imaging techniques including diffusion tensor imaging (DTI) or diffusion tensor spectroscopy (DTS), can be employed for this as well as methodological analytic methods using compressed sensing theory, machine learning, Bayesian approach, information theory and etc. These are the main academic goals in my Ph.D course.


 * Besides, tumor-imaging techniques with sufficient specificity and sensitivity are also of my interests, which guarantee super-high spatial and temporal resolution while background signals originated from non-target tissues and artifacts are minimized.


 * For a long-term plan, development of a new brain imaging technology based on physical backgrounds including radiological physics is one of the goals in my research career. The goal constantly evokes me to build extensive knowledge of physics in existing medical imaging modalities and to erudite insights of pioneering new field of functional brain imaging.

Education

 * 2011-now, Researcher, Dept. of Bio and Brain Engineering (majored in bio-imaging and signal processing), Korea Advanced Institute of Science and Technology (KAIST)
 * 2009-2011, MS, Dept. of Bio and Brain Engineering (majored in bio-imaging and signal processing), Korea Advanced Institute of Science and Technology (KAIST)
 * 2004-2009, BS, Dept. of Radiological Science (majored in nuclear medicine), Yonsei University (YSU)

Publications : Peer-reviewed journals
[3] Kangjoo Lee, Sungho Tak, and Jong Chul Ye, "A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion", ''IEEE Trans. Med. Imag.'', vol. 30, no. 5, pp. 1076-1089, May 2011

[2] Sungho Tak, Jaeduck Jang, Kangjoo Lee and Jong Chul Ye, "Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements,", Physics in Medicine and Biology, vol. 55, pp. 3249-3269, May 2010

[1] Yong Hyun Chung, Seung-Jae Lee, Cheol-Ha Baek, Kang-Joo Lee, and Yong Choi, "Characterization and Optimization of a Quasi-Monolithic Detector Module with Depth-Encoding for Small Animal PET", ''J. Korean Phys. Soc.'', vol. 54, no. 1, pp. 244-249, January 2009

Publications : Peer-reviewed conference proceedings
[8] Kangjoo Lee, Sungho Tak, Jong Chul Ye, "Sparse dictionary learning for fMRI activation detection using SPM and MDL criterion", The 17th Annual Meeting of the Organization for Human Brain Mapping (OHBM), June 2011, Quebec City, Canada

[7] Kangjoo Lee, Sungho Tak, Jong Chul Ye, "A data-driven spatially adaptive generalized linear model for functional MRI analysis", ''Proc. IEEE International Symposium of Biomedical Imaging (ISBI)'', March 2011, Chicago, Illinois, USA

[6] Kangjoo Lee, Sungho Tak, Jong Chul Ye, "A data-driven sparse GLM for a brain functional magnetic resonance imaging analysis", Korean Society for Human Brain Mapping (KHBM) Fall 2010 Conference, November 2010, Seoul, Republic of Korea

[5] Kangjoo Lee, Jong Chul Ye, "A Data-Driven fMRI Analysis using K-SVD Sparse Dictionary Learning", International Society of Magnetic Resonance in medicine (ISMRM), May 2010, Stockholm, Sweden

[4] Kangjoo Lee, Jong Chul Ye, "Statistical Parametric mapping of fMRI Data using Sparse Dictionary Learning", ''Proc. IEEE International Symposium of Biomedical Imaging (ISBI)'', April 2010, Rotterdam, Netherland

[3] Kangjoo Lee, Jong Chul Ye, "Sparse Dictionary Learning for Data-Driven fMRI Analysis", Korean 22th Workshop on Image Processing and Image Understanding (IPIU), February 2010, Jeju, Republic of Korea

[2]  Kang-Joo Lee, Jae-Wan Kim, Ki-Hong Son, Seung-Jae Lee, Cheol-Ha Baek, and Yong Hyun Chung, "Optimization of resolution uniformity with depth-encoding detector for small animal PET", 43th The Annual Meeting of Korean Radiological Technologists Association and 10th The East Asia Conference of Radiological Technologists, October 2008, Seoul, Republic of Korea

[1] Kang-Joo Lee, Jae-Wan Kim, Ki-Hong Son, Seung-Jae Lee, Cheol-Ha Baek, and Yong Hyun Chung, "Characterization of resolution uniformity in depth-encoding PET", The Korean Society of Radiology, May 2008, Wonju, Republic of Korea

Awards

 * Best Prize in Oral Contest, International Student Session, "Characterization of resolution uniformity in depth-encoding PET", The Korean Society of Radiology, May 2008, Wonju, Republic of Korea

License

 * A general license for managing of radioisotope (RI), Ministry of Education, Science and Technology, Republic of Korea, June 2007

Invited Talks

 * “Data-driven fMRI analysis using sparse dictionary learning”, KAIST/SNU Joint Workshop on Sparse Data Recovery and its Application to Medical Imaging, Medical Imaging Application Session, Seoul National University, Republic of Korea