Artificial Intelligence and Medical Imaging (AIMI) Lab.

Welcome to AIMI Lab @ POSTECH. We do cutting-edge research on Artificial Intelligence and Medical Imaging. We develop novel (statistical, machine learning, graph theoretical) models that are scalable and applicable to real-world imaging data including, but not limited to, medical images such as MRI, diffusion MRI, functional MRI and PET. We are particularly interested in Graph Learning Methods that can be applied to neuroimages in non-Euclidean spaces to identify the effects from neurodegenerative diseases and lead to better understanding of brain functions. We are also open for various other Vision and Machine Learning problems.

Please contact (without hyphen) if you are sufficiently self-motivated and interested in joining our group.


  • Jun, 2020. 2 papers accepted to MICCAI 2021. Congrats to Hyuna (POSTECH) and Fan (UTA)!
  • Jan, 2021. Our work on Learning multi-resolution representation of graph edges is accepted to IPMI 2021. Congrats Xin!
  • Dec, 2020. I joined POSTECH.
  • Aug, 2020. My proposal to NSF CRII (known as “mini CAREER”) is awarded.
  • Aug, 2020: Our proposal funded by NSF Core program, a joint work between UTA (Gautam Das and I) and NJIT (Senjuti B. Roy).
  • Jun, 2020. We have a paper accepted to Brain Connectivity in collaboration with UTSW.
  • July, 2020. We have a paper accepted to TPAMI in collaboration with UW-Madison.
  • May, 2020. We have a paper accepted to MICCAI 2020. Congrats Fan!
  • Jan, 2020. We have a paper accepted to ISBI 2020 for Oral Presentation. Congrats Xin!


#321, Science Building II, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongsangbuk-do, Korea