Members
Hongseok Kim
Hongseok is a Professor in the Department of Electronic Engineering @ Sogang University. He received his B.S. and M.S. degrees in the School of Electrical Engineering from Seoul National University, South Korea, and his Ph.D. degree in the Department of Electrical and Computer Engineering at The University of Texas at Austin, USA. He was a Post Doctoral Research Associate at Princeton University, and a Member of Technical Staff at Bell Laboratories Alcatel-Lucent, Murray Hill, USA. His current research interests include AI and machine learning, optimization and resource management in networks, specifically focused on energy ICT, power systems, smart grid communications, wireless networks and economics. Dr. Hongseok Kim is one of the two recipients of the Korea Government Oversea Scholarship during 2005-2008. He received the Haedong Young Professor Award in 2016. He has been an IEEE Senior Member since 2016.
Jaeseok Huh
Jaeseok is an Assistant Professor in the Department of Industrial & Information Systems Engineering at Soongsil University, where he leads the Simulation-based Decision-Making Lab (SDM Lab). His research focuses on solving scheduling and optimization problems by integrating simulation, reinforcement learning, metaheuristics, and data-driven analytics to address decision-making challenges in manufacturing and industrial systems. He earned his Ph.D. from Seoul National University, where he specialized in applying machine learning and optimization techniques to complex manufacturing systems. His doctoral research, conducted under the supervision of Prof. Jonghun Park, centered on intelligent scheduling methods for semiconductor manufacturing facilities.
His research expertise spans several key areas: scheduling optimization for semiconductor and manufacturing systems, simulation-driven decision-making for performance evaluation and policy design, reinforcement learning for autonomous decision-making policies, metaheuristic algorithms including particle swarm optimization and genetic algorithms, and machine learning combined with data analytics for predictive modeling. He has made significant contributions to semiconductor packaging facility scheduling, particularly in developing deep reinforcement learning approaches that can handle large-scale scheduling problems with complex constraints such as re-entrant flows, sequence-dependent setups, and alternative routes.
With over 400 citations to his research work, Prof. Huh has published extensively in leading journals including IEEE Transactions on Automation Science and Engineering, IEEE Access, and Sustainability. His notable publications include groundbreaking work on reinforcement learning approaches to robust scheduling of semiconductor manufacturing facilities and scalable scheduling methods using deep reinforcement learning. He has also contributed to research on unmanned combat aerial vehicles, large-scale automated storage and retrieval systems, and interactive visualization of manufacturing schedules.
His lab at Soongsil University continues to advance the integration of artificial intelligence, simulation, and optimization to solve real-world manufacturing challenges, with particular emphasis on semiconductor production systems and smart factory applications. His work bridges theoretical advances in machine learning and practical industrial applications, making him a valuable contributor to discussions on optimization, AI-driven decision-making, and the future of intelligent manufacturing systems.
Kihwan Choi
Kihwan is an Assistant Professor in the Department of Applied Artificial Intelligence at Seoul National University of Science and Technology (SeoulTech). His research focuses on developing artificial intelligence technologies for medical imaging, with particular expertise in CT imaging, endoscopy, image restoration, noise reduction, and automated diagnosis systems.
He earned his Ph.D. in Electrical Engineering from Stanford University (2014) where he worked under the supervision of Prof. Stephen Boyd and Lei Xing on compressed sensing and optimization methods for medical imaging. During his doctoral studies, he also completed an M.S. in Statistics (2012) and an earlier M.S. in Electrical Engineering (2008) from Stanford. He holds B.S. and M.S. degrees in Electrical and Computer Engineering from Seoul National University (2004, 2006). Before joining SeoulTech in 2023, Prof. Choi served as a Senior Researcher at the Korea Institute of Science and Technology (KIST) from 2017 to 2023, and as a Staff Researcher at Samsung Advanced Institute of Technology from 2014 to 2017. His industry experience includes developing AI-based medical imaging solutions and advanced optimization algorithms for clinical applications.
His research lab specializes in self-supervised learning-based image restoration and denoising techniques for low-dose CT imaging, addressing the critical challenge of minimizing patient radiation exposure while maintaining diagnostic image quality. This work overcomes the practical limitation of insufficient clinical training data by developing generalizable algorithms applicable across diverse clinical environments. Recent achievements include developing cyclic conditional diffusion models for cross-modal medical image synthesis and computer-aided diagnosis systems for colorectal cancer detection with explainable AI features to enhance clinical trust and usability.
Prof. Choi has published extensively in leading journals including Medical Physics, Expert Systems with Applications, IEEE Journal of Selected Topics in Signal Processing, and Physics in Medicine and Biology. His pioneering work on compressed sensing-based cone-beam CT reconstruction using first-order methods, developed during his Stanford doctorate, has been widely cited and influenced subsequent research in accelerated medical imaging. He has also contributed to research on brain-computer interface (BCI) platforms, AR/VR device control, and medical robotics systems.
Sunghee Yun
Sunghee is Co-founder & CTO @ Erudio Bio, Inc., CA, USA, Co-founder & CEO @ Erudio Bio Korea, Inc., Korea Leader of Silicon Valley Privacy-Preserving AI Forum (K-PAI), CGO / Global Managing Partner @ LULUMEDIC, KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, Salzburg, Austria, AI-Korean Medicine Integration Initiative Task Force Member @ The Association of Korean Medicine, an Visiting Professor of the Department of Electronic Engineering @ Sogang University, Seoul, South Korea, an Advisory Professor of the Department of Electrical Engineering & Computer Science (EECS) @ Daegu Gyeongbuk Institute of Science & Technology (DGIST), South Korea, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, Network Expert Consultant @ Gerson Lehrman Group, Inc., Chief Business Development Officer (CBDO) @ WeStory.ai, CA, USA, and Advisor @ CryptoLab, Inc.. He holds M.S. & Ph.D. in Electrical Engineering from Stanford University and B.S. in Electrical Engineering from Seoul National University.
Before founding the new AI biotech company, he co-founded Gauss Labs, Inc., an industrial AI company, built and ran the US Headquarters of Gauss Labs, spearheaded research & development of the core technology & products as CTO, Global Head of Research, Chief Applied Scientist & Senior Fellow. Before Gauss Labs, he drove various AI technology and product developments and productionizations in the field of e-Commerce @ Amazon.com, Inc. where one of his projects created a 200 million USD revenue increase via the Amazon Mobile Shopping App. Before Amazon, he worked for Software Research Center, Strategic Sales & Marketing Team, Design Technology (DT) Team & Computer-Aided Engineering (CAE) Team of Samsung Semiconductor, Inc. where he developed diverse AI and optimization tools for semiconductor chip designers, manufacturing engineers & test engineers where many engineers still use the circuit optimization tools and generic AI optimization platform, dubbed iOpt, every day.