We think critically and creatively.
The group's research interests in data science include
AI & machine learning (e.g., self-supervised learning, iterative neural networks, diffusion models, sparse representation learning, and domain adaptation),
optimization (e.g., non-convex optimization, block optimization, and proximal gradient methods), and
sampling (e.g., compressed sensing in multi-imager/sensor system and sampling optimization),
with past and current projects in imaging, image processing, and computer vision:
medical imaging (e.g., X-ray CT, MRI, PET, and SPECT),
computational photography (e.g., light-field photography, depth estimation, and 3D object tracking),
vision-based autonomous systems (e.g., end-to-end autonomous driving, visual SLAM, inter-vehicle distance estimation, anomaly detection using drone imaging),
biomedical image computing (e.g., abnormality detection on brain images and microscopic image segmentation), and
digital healthcare (e.g., depression/ADHD diagnosis).
We are interested both in developing state-of-the-art AI & data science solutions for these problems, as well as improving fundamental understanding of these solutions.
Il Yong Chun, Ph.D.
Il Yong Chun received the B.Eng. degree in Electrical Engineering from Korea University in 2009, and the Ph.D. degree in Electrical and Computer Engineering from Purdue University in 2015.
He joined the School of Electronic and Electrical Engineering at Sungkyunkwan University (SKKU) in 2021, as an Assistant Professor. Prior to joining SKKU, he was an Assistant Professor of Electrical and Computer Engineering at the University of Hawaiʻi, Mānoa, a Research Fellow in Electrical Engineering and Computer Science at The University of Michigan, and a Postdoctoral Research Associate in Mathematics at Purdue University, from 2019-2021, 2016-2019, and 2015-2016, respectively.
He is also affiliated with the Artificial Intelligence and Electrical and Computer Engineering departments.