People

Principal Investigator


Yoon, Young-Gyu [Google Scholar] ygyoon at kaist.ac.kr

Principal Investigator


Education
Ph. D. in EECS, MIT, 2018
M.S. in EE, KAIST, 2009
B.S. in EE, KAIST, 2007

Positions
Assistant professor, KAIST, 2018 - present
Postdoctoral associate, MIT, 2018
Research engineer, KAIST Institute, 2009 - 2012

Awards
Samsung Scholarship, 2012
IEEE CAS Guillemon-Cauer Best Paper Award, 2009
Topped the list at academic achievement test (Mathematics, Physics) among all freshmen of KAIST, 2003

Short Biography
Young-Gyu Yoon is an assistant professor of electrical engineering at KAIST and he holds a joint appointment in KAIST Institute for Health Science & Technology (KI HST). He is the leader of the KAIST Neuro-Instrumentation and Computational Analysis lab, an interdisciplinary research group focused on advancing neurotechnology, optical imaging, and computing.

He received the B.S. and M.S. degrees in EE from KAIST, Daejeon, Korea, in 2007 and 2009, respectively, and the Ph.D. degree in EECS from the Massachusetts Institute of Technology, Cambridge, MA, in 2018. He received the 2009 IEEE Transactions on Circuits and Systems Guillemon-Cauer Best Paper Award for his work at professor SeongHwan Cho's group on mixed-signal CMOS circuit design. For his doctoral study at professor Ed Boyden's group, he developed optical and computational tools for imaging and analyzing brain circuits. After his Ph.D. study, he briefly worked as a postdoctoral associate at MIT.

Graduate students


Cho, Eun-Seo [Google Scholar]

Ph.D. student

Research area

Imaging Brain Activity

Computational Imaging


Education
M.S. in EE, KAIST, 2021
B.S. in BME, Hanynang Univ., 2018

E-mail: eunseo.cho at kaist.ac.kr

Website: https://sites.google.com/view/eunseocho/main

Short Biography

Eun-Seo received his bachelor's degree in Biomedical Engineering from Hanyang University and his master’s degree in Electrical Engineering from KAIST. Currently, he is a Ph.D. student in NICA lab and he works on developing novel optical imaging systems to image the neural activity of the entire brain with high spatial and temporal resolutions.

Publications

*co-first authors

  • M. Eom*, S. Han*, G. Kim, E.-S. Cho, J. Sim, P. Park, K.-H. Lee, S. Kim, M. Rózsa, K. Svoboda, M. Choi, C.-H. Kim, A.E. Cohen, J.-B. Chang, Y.-G. Yoon, Statistically unbiased prediction enables accurate denoising of voltage imaging data, bioRxiv, 2022.

  • J. Cho*, S. Han*, E.-S. Cho, K. Shin, Y.-G. Yoon, Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.

  • C. Shin*, H. Ryu*, E.-S. Cho, S. Han, K.-H. Lee, C.-H. Kim, Y.-G. Yoon. Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network, Medical Image Analysis, 2022.

  • J. Sim* , C. E Park* , I. Cho* , K. Min, M. Eom, S. Han, H. Jeon, H.-J. Cho, E.-S. Cho, A. Kumar, Y. Chong, J. S. Kang, K. D. Piatkevich, E. E. Jung, D.-S. Kang, S.-K. Kwon, J. Kim, K.-J. Yoon, J.-S. Lee, E. S. Boyden, Y.-G. Yoon**, J.-B. Chang**, Nanoscale resolution imaging of the whole mouse embryos and larval zebrafish using expansion microscopy, bioRxiv, 2022.

  • E.-S. Cho*, S. Han*, K.-H. Lee, C.-H. Kim, Y.-G. Yoon. 3DM: Deep decomposition and deconvolution microscopy for rapid neural activity imaging, Optics Express, 2021. (highlighted as Editors' Pick)

  • S. Han, E.-S. Cho, I. Park, K. Shin, Y.-G. Yoon. Efficient Neural Network Approximation of Robust PCA for Automated Analysis of Calcium Imaging Data, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.

  • C. Shin*, H. Ryu*, E.-S. Cho, Y.-G. Yoon. RLP-Net: A Recursive Light Propagation Network for 3-D Virtual Refocusing, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.


Han, Seungjae [Google Scholar]

Ph.D. student

Research area

Computational Imaging

Neuro-image Processing


Education

B.S. in SIT, Yonsei Univ., 2020

E-mail: jay0118 at kaist.ac.kr

Website: https://stevejayh.github.io/

Short Biography

Seungjae is a Ph.D. student in Neuro-Instrumentation & Computational Analysis (NICA) lab at KAIST. He received his bachelor's degree in Integrated Technology from Yonsei University. He is developing novel methods to acquire and analyze calcium imaging data.

Publications

*co-first authors

  • H. Kim*, S. Bae*, J. Cho, H.-Y. Nam, J. Seo, S. Han, E. Yi, E. Kim, Y.-G. Yoon**, J.-B. Chang**, IMPASTO : Multiplexed cyclic imaging without signal removal via self-supervised neural unmixing, bioRxiv, 2022.

  • M. Eom*, S. Han*, G. Kim, E.-S. Cho, J. Sim, P. Park, K.-H. Lee, S. Kim, M. Rózsa, K. Svoboda, M. Choi, C.-H. Kim, A.E. Cohen, J.-B. Chang, Y.-G. Yoon, Statistically unbiased prediction enables accurate denoising of voltage imaging data, bioRxiv, 2022.

  • J. Cho*, S. Han*, E.-S. Cho, K. Shin, Y.-G. Yoon, Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.

  • C. Shin*, H. Ryu*, E.-S. Cho, S. Han, K.-H. Lee, C.-H. Kim, Y.-G. Yoon. Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network, Medical Image Analysis, 2022.

  • J. Sim* , C. E Park* , I. Cho* , K. Min, M. Eom, S. Han, H. Jeon, H.-J. Cho, E.-S. Cho, A. Kumar, Y. Chong, J. S. Kang, K. D. Piatkevich, E. E. Jung, D.-S. Kang, S.-K. Kwon, J. Kim, K.-J. Yoon, J.-S. Lee, E. S. Boyden, Y.-G. Yoon**, J.-B. Chang**, Nanoscale resolution imaging of the whole mouse embryos and larval zebrafish using expansion microscopy, bioRxiv, 2022.

  • E.-S. Cho*, S. Han*, K.-H. Lee, C.-H. Kim, Y.-G. Yoon. 3DM: Deep decomposition and deconvolution microscopy for rapid neural activity imaging, Optics Express, 2021. (highlighted as Editors' Pick)

  • S. Han, E.-S. Cho, I. Park, K. Shin, Y.-G. Yoon. Efficient Neural Network Approximation of Robust PCA for Automated Analysis of Calcium Imaging Data, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.


Kim, Gyuri [Google Scholar]

Ph.D. student

Research area

Imaging Brain Activity

Neuro-data Mining

Education

B.S. in EE, DGIST, 2021

E-mail: gyuri2102 at kaist.ac.kr

Website:

Short Biography

Gyuri is a Ph.D. student in Neuro-Instrumentation & Computational Analysis (NICA) lab at KAIST. She received her bachelor's degrees in School of Undergraduate Studies (major in Electrical Engineering) at DGIST.

Publications

*co-first authors

  • M. Eom*, S. Han*, G. Kim, E.-S. Cho, J. Sim, P. Park, K.-H. Lee, S. Kim, M. Rózsa, K. Svoboda, M. Choi, C.-H. Kim, A.E. Cohen, J.-B. Chang, Y.-G. Yoon, Statistically unbiased prediction enables accurate denoising of voltage imaging data, bioRxiv, 2022.



Eom, Minho [Google Scholar]

M.S. student

Research area

Computational Imaging

Neuro-image Processing

Neuro-data Mining


Education

B.S. in CS&EE, KAIST, 2021

E-mail: djaalsgh159 at kaist.ac.kr

Website: https://www.minhoeom.com/

Short Biography

Minho Eom is a graduate student in the School of Electrical Engineering at KAIST, and a researcher in Neuro-Instrumentation & Computational Analysis (NICA) lab. His research in computational neuroscience focuses on discovering the mechanism of a brain function: computational analysis of neuronal data, processing of neuronal images, and implementing neuro-instruments. He received his B.S. degrees in Computer Science and Electrical Engineering from KAIST, 2021.

Publications

*co-first authors

  • M. Eom*, S. Han*, G. Kim, E.-S. Cho, J. Sim, P. Park, K.-H. Lee, S. Kim, M. Rózsa, K. Svoboda, M. Choi, C.-H. Kim, A.E. Cohen, J.-B. Chang, Y.-G. Yoon, Statistically unbiased prediction enables accurate denoising of voltage imaging data, bioRxiv, 2022.

  • J. Sim*, C. E Park*, I. Cho*, K. Min, M. Eom, S. Han, H. Jeon, H.-J. Cho, E.-S. Cho, A. Kumar, Y. Chong, J. S. Kang, K. D. Piatkevich, E. E. Jung, D.-S. Kang, S.-K. Kwon, J. Kim, K.-J. Yoon, J.-S. Lee, E. S. Boyden, Y.-G. Yoon**, J.-B. Chang**, Nanoscale resolution imaging of the whole mouse embryos and larval zebrafish using expansion microscopy, bioRxiv, 2022.



Bae, Seoungbin [Google Scholar]

M.S. student

Research area

Multiplexed & Super-resoltuion Imaging

Neuro-image Processing


Education

B.S. in Cyber Defense, Korea Univ., 2021

E-mail: sbbae31 at kaist.ac.kr

Website:

Short Biography

Seoungbin is a master student in Neuro-Instrumentation & Computational Analysis (NICA) lab at KAIST. He received his bachelor's degree in Cyber Defense from Korea University.

Publications

*co-first authors

  • H. Kim*, S. Bae*, J. Cho, H.-Y. Nam, J. Seo, S. Han, E. Yi, E. Kim, Y.-G. Yoon**, J.-B. Chang**, IMPASTO : Multiplexed cyclic imaging without signal removal via self-supervised neural unmixing, bioRxiv, 2022.



Lee, Minyoung [Google Scholar]

M.S. student

Research area

Medical AI

Neuro-image Processing


Education

B.S. in EE, Korea Univ., 2022

E-mail: 520alsdud at kaist.ac.kr

Website:

Short Biography

Minyoung is a master student in Neuro-Instrumentation & Computational Analysis (NICA) lab at KAIST. She received her bachelor's degrees in Electrical Engineering from Korea University in 2022.

Publications

*co-first authors




You, Joshua Yedam [Google Scholar]

M.S. student

Research area

Computational Imaging

Neuro-image Processing


Education

B.S. in CPE, University of Virginia, 2020

E-mail: jyy20422 at kaist.ac.kr

Website: https://sites.google.com/view/joshyou/

Short Biography

Josh is currently a master student at the Neuro-Instrumentation & Computational Analysis (NICA) lab at KAIST, supervised by Dr. Young-Gyu Yoon. He received his Bachelor of Science in Computer Engineering with high distinction at the University of Virginia, Charlottesville, VA, USA. After receiving his Bachelor’s degree, he worked as research engineer at the National Institute of Standards & Technology (NIST) in Gaithersburg, MD, USA through the NIST Professional Research Experience Program (PREP) with the University of Maryland, College Park. He is interested in applying computational methodologies such as machine learning and computer vision to understand neurological activity and behavior.

Publications

*co-first authors




Ahn, Sungjin [Google Scholar]

M.S. student (starting from Spring 2023)

Research area


Education

B.S. in EE, KAIST, 2023

E-mail: boku109 at kaist.ac.kr

Website:

Short Biography


Publications

*co-first authors




Kim, Soi [Google Scholar]

M.S. student (starting from Spring 2023)

Research area



Education

B.S. in EE, Soongsil Univ., 2022

E-mail: eleksy25 at kaist.ac.kr

Website:

Short Biography


Publications

*co-first authors



Undergraduate students


Chung, Yoonjae

B.S. student

Education

Majoring in EE, KAIST


Ryu, Hyun

B.S. student

Education

Majoring in EE, KAIST


Yu, Ha-yeong

B.S. student

Education

Majoring in EE, KAIST

Alumni: graduate students


Kang, Woosuk [Google Scholar]

M.S.

Education

M.S. in EE, KAIST, 2021

B.S. in EE, KAIST, 2019


Cho, Junmo [Google Scholar]

M.S.

Education

M.S. in EE, KAIST, 2022

B.S. in EE&CS (Minor Math), KAIST, 2020

E-mail: junmokane at kaist.ac.kr

Website:

Short Biography

Junmo was a master student in Neuro-Instrumentation & Computational Analysis (NICA) lab at KAIST. He received his bachelor's degrees in Electrical Engineering and Computer Science from KAIST and his master’s degree in Electrical Engineering from KAIST. His research mainly focused on implementing bio-inspired AI using deep reinforcement learning. He also developed neural networks for analyzing calcium imaging data.

Publications

*co-first authors

  • H. Kim*, S. Bae*, J. Cho, H.-Y. Nam, J. Seo, S. Han, E. Yi, E. Kim, Y.-G. Yoon**, J.-B. Chang**, IMPASTO : Multiplexed cyclic imaging without signal removal via self-supervised neural unmixing, bioRxiv, 2022.

  • J. Cho, D.-H. Lee, Y.-G. Yoon. Inducing Functions through Reinforcement Learning without Task Specification, Neural Information Processing Systems Workshop (NeurIPS Workshop) on Deep Reinforcement Learning, 2022.

  • J. Cho*, S. Han*, E.-S. Cho, K. Shin, Y.-G. Yoon, Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.


Shin, Changyeop [Google Scholar]

M.S.

Education

M.S. in EE, KAIST, 2022

B.S. in EECS, GIST, 2017

E-mail: scey26 at kaist.ac.kr

Website:

Short Biography

Changyeop was a master student in Neuro-Instrumentation & Computational Analysis (NICA) lab at KAIST, supervised by prof. Young-Gyu Yoon. He received his bachelor’s degree in Electrical Engineering and Computer Science from GIST in 2017 and his master’s degree in Electrical Engineering from KAIST in 2022. He was a research officer of national defense at Agency for Defense Development (ADD) in 2017 - 2020. His research interests included biomedical image computing and machine learning with a focus on image reconstruction and domain adaptation.

Publications

*co-first authors

  • C. Shin*, H. Ryu*, E.-S. Cho, S. Han, K.-H. Lee, C.-H. Kim, Y.-G. Yoon. Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network, Medical Image Analysis, 2022.

  • C. Shin*, H. Ryu*, E.-S. Cho, Y.-G. Yoon. RLP-Net: A Recursive Light Propagation Network for 3-D Virtual Refocusing, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.

Alumni: visiting graduate students


Liu, Huiling

M.S. student

Main affiliation

Chongqing University of Technology (CQUT)


Education

Majoring in Information and communication engineering, Chongqing University of Technology (CQUT)

B.S. in Communication engineering, Suzhou University, 2019

Alumni: undergraduate students

Jeon, Jiwon

(Spring 2019-Summer 2019)

Jeon, Sang Eon
(Spring 2020)

Jeong, Jaewon
(Summer 2020)

Nam, Seo Yeon

(Summer 2020)

Jung, Hohurn
(Summer 2020)

Park, Gyutae
(Fall 2020)

Lee, Inhee

(Fall 2020, Winter 2021-Spring 2022)

Lee, Chang Sun
(Summer 2020-Winter 2020)

Lee, Jeongwon
(Winter 2020-Spring 2021)

Cho, Eun Yeong

(Winter 2020-Spring 2021)

Ryu, Hyun
(Spring 2020-Summer 2021)

Suh, Teokkyu
(Spring 2021-Summer 2021)

Lee, Seyeon

(Fall 2020-Fall 2021)

Kim, Eunsu

(Winter 2020-Fall 2021)

Bae, Jeongin

(Spring 2021-Fall 2021)

Yi, Euiin

(Summer 2021-Winter 2021)

Jeon, Minsik

(Fall 2021-Winter 2021)

Kim, Dawon

(Winter 2021)

Lee, Sanghun

(Fall 2021-Spring 2022)

Lee, Gyuwon

(Winter 2021-Spring 2022, Fall 2022)

Lee, Sowoo

(Spring 2022-Summer 2022)

Park, Mingi

(Spring 2022)

Huh, Chan

(Spring 2022)

Hwang, Namun

(Spring 2022)

Ahn, Sungjin

(Summer 2022-Fall 2022)

Choi, Jinsung

(Summer 2022-Fall 2022)

Park, Jong Geon

(Fall 2022)