AI Convergence Lab at Chungbuk National University. We turn Applied AI, Computer Vision and Quantum Computing research into solutions for industry.
AICLab was established to pursue Applied AI, Computer Vision and Quantum Computing for industry and academic purposes. Together with our partners and clients, we work on the pain points and operational inefficiencies where these technologies make a measurable difference.
Our lab brings together passionate researchers from diverse backgrounds, united by a common vision to create impactful solutions that bridge academic excellence with industry needs.
We set our target as broad as the peak of the mountain.
We pursue that dream while holding our work to a high standard.
We get there together, with people who share the same vision.
Recent achievements and milestones from the lab.
Sep 2026
Sep 2026
Sep 2026
Faculty, researchers and students pursuing excellence together.
Principal Investigator
Collaborator
Collaborator
Researcher
Quantum Computing, Compilers
Researcher
Computer Vision, LLM, VLM
Researcher
Computer Vision, LLM, VLM
PhD Student
Big Data
PhD Student
Quantum Computing, Quantum Machine Learning
Peer-reviewed work in journals and at conferences.
Vungsovanreach Kong, Seokwon Taylor Yoon, Jeong-Hun Won, Tae-Kyung Kim
IEEE Access, Early Access, Sep. 2026
Sokheang Chan, Naro Vorn, Vungsovanreach Kong, Chang Kue Seok, Jong-Hyun Kim, Anand Nayyar, Tae-Kyung Kim
Array, Vol. 31, 101135, Sep. 2026
Kimchhor Chiv, Anand Nayyar, Tae-Kyung Kim
ICT Express, Aug. 2026
The project aims to develop an intelligent bin-picking system that enables robots to detect, localize, and grasp objects in cluttered, unstructured environments. By combining advanced vision techniques such as instance segmentation, graph-transformer reasoning, depth refinement, shape completion, and 6D pose estimation, the project seeks to overcome challenges like occlusion and sensor noise.
This research develops an AI system that converts natural-language questions into accurate SQL queries, removing SQL expertise as a barrier to data access. The research focuses on schema understanding, query validation, hallucination reduction, and automated dataset curation using multiple AI agents. The lab's MRS-Agent submission reached rank #21 on the BIRD benchmark, a large-scale cross-domain Text-to-SQL evaluation, achieving 75.13% execution accuracy on the test set. The same methods apply naturally to scientific data access, where large research databases remain difficult to query without specialized training.
This research explores quantum annealing and hybrid quantum-classical methods for tackling highly complex, large-scale optimization problems that are difficult for conventional computing approaches. The work aims to apply these methods to domain-specific challenges such as synchrotron accelerator scheduling, parameter optimization, resource allocation, and operational control. Because these optimization methods generalize across domains, the lab is also interested in computationally hard problems in molecular modeling and structural analysis relevant to the life sciences.
How we learn, share and spend time together.
