[Deprecated] Algorithmic Machine Intelligence Lab (AMI Lab.) @ POSTECH

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[ Notice ] AMI Lab is looking for self-motivated prospective 'Grad Students'. If you are interested in joining AMI Lab, please send an official transcript (including ranking information) and CV to sungbin@postech.ac.kr AMI Lab에서는 열정 넘치는 '대학원생 연구원' 을 모집하고 있습니다. AMI Lab에 관심 있으신 지원자 분들은 입학 및 연구참여 담당자 (sungbin@postech.ac.kr) 에게 석차가 표기된 공식 성적증명서와 본인의 CV를 첨부해 먼저 연락해주세요.
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ECCV’22] Three papers have been accepted
ECCV’22] Three papers have been accepted
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Current Members


Master Alumni

Lee Hyun MS, 2024 Samsung Advanced Institute of Technology (SAIT)
Kim Sung-Bin MS, 2023 POSTECH
Kwon Byung-Ki MS, 2023 POSTECH
Hyunwoo Ha MS, 2023 POSTECH
Nam Hyeon-Woo MS, 2022 POSTECH
Moon Ye-Bin MS, 2022 POSTECH


Do Huu Dat Undergrad. (Foreign) Intern, 2024 Now at Vin Univ. as undergrad. student
Lee Chae-Yeon Researcher, 2023 Now at POSTECH as master student
Jisoo Kim Undergrad. Intern, 2023 Now at Columbia Univ. as master student
Lee Hyun Researcher, 2022 Samsung Advanced Institute of Technology (SAIT)
Kim Ji-Yun Researcher, 2022 Now at Krafton as infra engineer
Han Byeol-Yi Visiting Ph.D. student, 2021 Georgia Tech as Ph.D. student
Kim Sung-Bin Researcher, 2021 Now at POSTECH as Ph.D. student
Jieun Lee Postdoc, 2020 Now at KERIS as researcher
Kyungdon Joo Postdoc, 2020 Now at UNIST as assistant professor


AMILab mainly focuses on the following research agenda: * Machine perception understanding - Multi-modal learning - Video (visual and temporal) understanding - 3D perception understanding * Data efficient learning - Prior based learning and inference - Minimal-supervised learning - Learning with human-in-the-loop
We believe that, in order to achieve Artificial Generic Intelligence, Sensing and Efficient Learning are the most important and fundamental blocks needed to be done first.
We are working toward developing machine perception capability by understanding human perception capability; thereby, intelligent agents can efficiently learn to understand about the real-world.
Besides this, AMILab members have broad research interests in Computer Vision & Machine Learning. In particular, we put a high priority on the research that connects human and machine, i.e., human centric research.
Lastly, we are interested in the following general topics as well. * Deep neural networks * Invariant representation learning * Low-rank & sparse structure, and related optimization techniques including compressed sensing


To check out AMI Lab's publication list, please refer to this page
Information about computer vision and machine learning academic field * Top-tier conferences : CVPR, ICCV, ECCV, NeurIPS (NIPS), ICML, and ICLR are considered as high prestigious and top-tier conferences, which deem to have larger impacts than most SCI journals. According to Google scholar metrics, all these conferences are listed in the top 100 in all academic fields. Out of them, CVPR is the 5th rank (Science and Nature are the 1st and 3rd ranked) among all academic fields, In terms of the acceptance rate, oral presentations are about <4% and poster presentations about 25%, i.e., highly competitive. * Top-tier journals : IEEE TPAMI and IJCV have the highest impact factors across all computer science categories. As of 2020, the impact factor of TPAMI is 17.861.


경상북도 포항시 남구 청암로 77 포항공과대학교 공학 2동 508-2호 (Unit 508-2, Engineering Building II, Cheongam-ro, Nam-gu, Pohang-si, Gyeongsangbuk-do, Republic of Korea)
Phone: 054-279-5934 Admission: sungbin@postech.ac.kr URP: hyunlee@postech.ac.kr
AMI Lab Weekly Seminar: AMI Lab YouTube