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Intelligence from
incomplete
measurements.

We build mathematically grounded AI that sees beyond limited data—from biomedical images to embodied perception.

Explore research ↓Join the lab ↗

Department of Robotics & Mechatronics Engineering | DGIST

01 / Team & Leadership

Seung Kwan Kang, Ph.D.

Assistant Professor · DGIST

An medical imaging and AI researcher working across inverse problems, AI, and clinical translation. Previously AI Research Director at Brightonix Imaging and Investment Director at Samsung Ventures.

skang@dgist.ac.kr ↗Student & Postdoc Openings ↓

Education

2014 – 2021
Ph.D. in Biomedical SciencesSeoul National University College of Medicine
2010 – 2014
B.S. in Electrical and Computer EngineeringSeoul National University

Professional Experience

2026 – present
Assistant ProfessorDGIST · Department of Robotics & Mechatronics Engineering
2026
Investment DirectorSamsung Ventures
2021 – 2026
Director, AI/AlgorithmBrightonix Imaging Inc.

Honors & Awards

2025
NMMI Outstanding Research AwardKorean Society of Nuclear Medicine
2025
Outstanding Paper Award in Science and TechnologyKorean Federation of Science and Technology Societies
2021
Outstanding Graduate AwardSeoul National University
2020
Young Investigator Award (1st place)Korean Society of Nuclear Medicine
2017 – 2020
Education GrantsIEEE NSS/MIC · IEEE NPSS (×6)

Ph.D. Students

Research interests. Profile coming soon.

M.S. Students

Research interests.Profile coming soon.

Undergraduate Students

Research interests.Profile coming soon.

We are recruiting Ph.D., M.S., and undergraduate students — see thePh.D. andM.S. program pages, orcontact us for undergraduate research opportunities.

02 / Research

One idea,
three directions.

Infer what matters when measurements are noisy, sparse, or incomplete.

Inverse problems & generative learning

We design principled algorithms that recover structure—and uncertainty—from incomplete measurements.

Inverse problems
Diffusion & flow
Posterior sampling

Biomedical imaging & quantification

We turn PET, SPECT, CT, and MRI into reliable quantitative evidence for research and clinical decisions.

PET / SPECT
Quantitative AI
Clinical translation

Embodied perception & robotics

We extend measurement-aware intelligence to systems that must perceive, reason, and act in the physical world.

Robot learning
VLA
Perception–action

03 / Publications

Theory that
travels.

2026Journal of Nuclear Medicine· doi:10.2967/jnumed.125.271172

Featured

Development and first-in-human imaging results of PHAROS: a versatile high-resolution TOF/DOI PET scanner for brain, breast, and extremity imaging

GB Ko, KY Kim, SK Kang, J Lee, R Jung, DJ Kwak, J-W Son, SA Shin, JS Lee

PET Instrumentation

2025Journal of Nuclear Medicine· 66(11): 1827–1833

Co-First Author

Artificial intelligence–powered quantification of flortaucipir PET for detecting tau pathology

HB Yoo†, SK Kang†, SA Shin, D Kim, H Choi, YK Kim, D Yi, MS Byun, DY Lee, JS Lee

Tau PET & Quantitative AI

2024British Journal of Radiology· 97(1155): 632–639

Co-First Author

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network

JG Nam†, SK Kang†, H Choi, W Hong, J Park, JM Goo, JS Lee, CM Park

Super-Resolution & Data Reduction

2024Nuclear Medicine and Molecular Imaging· 58(4): 246–254

First Author

Clinical performance evaluation of an artificial intelligence-powered amyloid brain PET quantification method

SK Kang, M Heo, JY Chung, D Kim, SA Shin, H Choi, A Chong, J-M Ha, H Kim, JS Lee

Clinical AI & Amyloid PET

2024Nuclear Medicine and Molecular Imaging· 58(6): 354–363

First Author

Accurate automated quantification of dopamine transporter PET without MRI using deep learning-based spatial normalization

SK Kang, D Kim, SA Shin, YK Kim, H Choi, JS Lee

MRI-Free Quantification for DAT PET

2024Journal of Nuclear Medicine· 65(10): 1645–1651

Co-Corresponding

Improving 18F-FDG PET quantification through a spatial normalization method

D Kim, SK Kang*, SA Shin, H Choi, JS Lee

FDG PET Quantification

2024Physics in Medicine & Biology· 69(21): 215020

Enhancing bone scan image quality: an improved self-supervised denoising approach

SY Yie, SK Kang, J Gil, D Hwang, H Choi, YK Kim, JC Paeng, JS Lee

Self-Supervised Denoising

2023Nuclear Medicine and Molecular Imaging· 57(2): 86–93

Automatic lung cancer segmentation in [18F]FDG PET/CT using a two-stage deep learning approach

J Park, SK Kang, D Hwang, H Choi, S Ha, JM Seo, JS Eo, JS Lee

Lesion Segmentation

2023Journal of Nuclear Medicine· 64(4): 659–666

First Author

Fast and accurate amyloid brain PET quantification without MRI using deep neural networks

SK Kang, D Kim, SA Shin, YK Kim, H Choi, JS Lee

Deep Learning-based Quantification

2023Frontiers in Nuclear Medicine· 3: 1326549

Editorial

Editorial: Rising stars in PET and SPECT: 2022

D Izquierdo-Garcia, SK Kang

Editorial

2022European Journal of Nuclear Medicine and Molecular Imaging· 49(6): 1833–1842

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography

D Hwang, SK Kang, KY Kim, H Choi, JS Lee

Attenuation Correction

2022European Journal of Nuclear Medicine and Molecular Imaging· 49(9): 3061–3072

Image-level trajectory inference of tau pathology using variational autoencoder for flortaucipir PET (for Alzheimer's Disease Neuroimaging Initiative)

J Hong, SK Kang, I Alberts, J Lu, R Sznitman, JS Lee, A Rominger, H Choi, K Shi

Generative VAE & Trajectory

2021Molecular Psychiatry· 26(7): 3476–3488

Dopamine dysregulation in psychotic relapse after antipsychotic discontinuation: an [18F]DOPA and [11C]raclopride PET study in first-episode psychosis

S Kim, SH Shin, B Santangelo, M Veronese, SK Kang, JS Lee, GJ Cheon, W Lee, JS Kwon, OD Howes

Clinical Neuroimaging

2021Scientific Reports· 11(1): 1673

Co-First Author

Deep learning-based 3D inpainting of brain MR images

SK Kang†, SA Shin, S Seo, MS Byun, DY Lee, YK Kim, DS Lee, JS Lee

3D Image Inpainting

2021NeuroImage· 232: 117890

First Author

Translating amyloid PET of different radiotracers by a deep generative model for interchangeability (for Alzheimer's Disease Neuroimaging Initiative)

SK Kang, H Choi, JS Lee

Deep Generative Translation

2021Physics in Medicine & Biology· 66(9): 095010

First Author

Anatomy-guided PET reconstruction using the ℓ1-Bowsher prior

SK Kang, JS Lee

Inverse problem & PET Reconstruction

2021Physics in Medicine & Biology· 66(11): 115009

Data-driven respiratory phase-matched PET attenuation correction without CT

D Hwang, SK Kang, KY Kim, H Choi, S Seo, JS Lee

CT-Free Attenuation Correction

2021Biomedical Engineering Letters· 11(3): 263–271

Co-First Author

Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy

SK Kang†, HJ An, H Jin, J Kim, EK Chie, JM Park, JS Lee

CycleGAN & Cross-Modality

2021Electronics· 10(13): 1529

First Author

Noise2Noise improved by trainable wavelet coefficients for PET denoising

SK Kang, SY Yie, JS Lee

Self-Supervised Denoising

2021Electronics· 10(15): 1836

Accurate transmission-less attenuation correction method for amyloid-β brain PET using deep neural network

BH Choi, D Hwang, SK Kang, KY Kim, H Choi, S Seo, JS Lee

Deep Attenuation Correction

2020Physica Medica· 72: 60–72

First Author

Robust nonlinear parameter estimation in tracer kinetic analysis using infinity norm regularization and particle swarm optimization

SK Kang, S Seo, C-H Lee, MJ Kim, SJ Kim, JS Lee

Tracer Kinetic Modeling

2020Nuclear Medicine and Molecular Imaging· 54: 299–304

Self-supervised PET denoising

SY Yie, SK Kang, D Hwang, JS Lee

Denoising

2019Journal of Nuclear Medicine· 60(8): 1183–1189

Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps

D Hwang, SK Kang, KY Kim, S Seo, JC Paeng, DS Lee, JS Lee

Deep Attenuation Correction

2018Physics in Medicine & Biology· 63(11): 115015

Novel inter-crystal scattering event identification method for PET detectors

MS Lee, SK Kang, JS Lee

Detector Physics

2018Journal of Nuclear Medicine· 59(10): 1624–1629

Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning

D Hwang, KY Kim, SK Kang, S Seo, JC Paeng, DS Lee, JS Lee

Reconstruction & Deep Learning

2018Human Brain Mapping· 39(9): 3769–3778

First Author

Adaptive template generation for amyloid PET using a deep learning approach

SK Kang, S Seo, SA Shin, MS Byun, DY Lee, YK Kim, DS Lee, JS Lee

Adaptive Deep Templates

2018Physics in Medicine & Biology· 63(14): 145011

Computed tomography super-resolution using deep convolutional neural network

J Park, D Hwang, KY Kim, SK Kang, YK Kim, JS Lee

CT Super-Resolution

2017Anticancer Research· 37(3): 1139–1148

Co-First Author

Relationship between Ktrans and K1 with simultaneous versus separate MR/PET in rabbits with VX2 tumors

KH Lee†, SK Kang†, JM Goo, JS Lee, GJ Cheon, S Seo, EJ Hwang

Kinetic Parameters & PET/MR

04 / Projects

Ongoing
research.

To be announced

Upcoming research grants led by the lab will be listed here once they officially begin.

Completed Research

P1Korea Health Industry Development Institute (KHIDI)

Brain Aid on Your Smartphone (BAYS)

Development of a healthy aging service using a brain age and cognitive reserve-based foundation model and a digital therapeutics platform.

RolePrincipal Investigator
GrantRS-2025-25455095

Brain Age
Cognitive Reserve
Digital Therapeutics

P2Korea Technology and Information Promotion Agency for SMEs (TIPA)

AI Solution for Radioisotope Therapy Dosimetry

Development of an AI solution and platform for internal radioisotope therapy dosimetry and treatment planning.

RolePrincipal Investigator
GrantRS-2025-02222237

Dosimetry
Treatment Planning
Nuclear Medicine

P3Korea Dementia Research Center

Longitudinal Neuroimaging Analysis for Dementia

Development of an AI-based longitudinal neuroimaging analysis platform for monitoring dementia progression and treatment response.

RolePrincipal Investigator
GrantRS-2023-KH136195
WithSeoul National University Hospital

Longitudinal Analysis
Neuroimaging
Dementia

Join the Lab · For Students

We are looking for motivated undergraduate interns, graduate students (M.S. / Ph.D.), and postdocs interested in AI, inverse problems, medical imaging, and robotics.

Learn Rigorous mathematical foundations & deep learning systems
Build Reproducible algorithms with real clinical and robotic datasets
Publish Top-tier papers in AI, medical imaging, and robotics venues

Inquire about positions ↗

Intelligence fromincompletemeasurements.

Seung Kwan Kang, Ph.D.

One idea,three directions.

Inverse problems & generative learning

Biomedical imaging & quantification

Embodied perception & robotics

Theory thattravels.

Development and first-in-human imaging results of PHAROS: a versatile high-resolution TOF/DOI PET scanner for brain, breast, and extremity imaging

Artificial intelligence–powered quantification of flortaucipir PET for detecting tau pathology

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network

Clinical performance evaluation of an artificial intelligence-powered amyloid brain PET quantification method

Accurate automated quantification of dopamine transporter PET without MRI using deep learning-based spatial normalization

Improving 18F-FDG PET quantification through a spatial normalization method

Enhancing bone scan image quality: an improved self-supervised denoising approach

Automatic lung cancer segmentation in [18F]FDG PET/CT using a two-stage deep learning approach

Fast and accurate amyloid brain PET quantification without MRI using deep neural networks

Editorial: Rising stars in PET and SPECT: 2022

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography

Image-level trajectory inference of tau pathology using variational autoencoder for flortaucipir PET (for Alzheimer's Disease Neuroimaging Initiative)

Dopamine dysregulation in psychotic relapse after antipsychotic discontinuation: an [18F]DOPA and [11C]raclopride PET study in first-episode psychosis

Deep learning-based 3D inpainting of brain MR images

Translating amyloid PET of different radiotracers by a deep generative model for interchangeability (for Alzheimer's Disease Neuroimaging Initiative)

Anatomy-guided PET reconstruction using the ℓ1-Bowsher prior

Data-driven respiratory phase-matched PET attenuation correction without CT

Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy

Noise2Noise improved by trainable wavelet coefficients for PET denoising

Accurate transmission-less attenuation correction method for amyloid-β brain PET using deep neural network

Robust nonlinear parameter estimation in tracer kinetic analysis using infinity norm regularization and particle swarm optimization

Self-supervised PET denoising

Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps

Novel inter-crystal scattering event identification method for PET detectors

Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning

Adaptive template generation for amyloid PET using a deep learning approach

Computed tomography super-resolution using deep convolutional neural network

Relationship between Ktrans and K1 with simultaneous versus separate MR/PET in rabbits with VX2 tumors

Ongoingresearch.

Brain Aid on Your Smartphone (BAYS)

AI Solution for Radioisotope Therapy Dosimetry

Longitudinal Neuroimaging Analysis for Dementia

Join the Lab · For Students

Intelligence from
incomplete
measurements.

One idea,
three directions.

Theory that
travels.

Ongoing
research.