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"AI For Good via Accessible, Scalable, and Reliable Intelligence"
OUR Mission: Understanding and Engineering Knowledge in AI Models
OUR Mission: Understanding and Engineering Knowledge in AI Models
Our mission is to advance artificial intelligence by understanding and engineering how knowledge is learned, represented, retained, and used within AI models.
Rather than focusing solely on building ever-larger models, we aim to develop AI that can accumulate knowledge over time, adapt to changing environments, and make effective use of what it has learned. Toward this goal, we pursue AI models that are:
(Accessible) Learned knowledge can be understood, extracted, transferred, and reused.
(Scalable) Models can efficiently learn, retain, and adapt knowledge throughout their lifetime.
(Reliable) Learned knowledge can be examined, corrected, reorganized, or removed when necessary.
From an academic perspective, we pursue research that is theoretically rigorous, computationally efficient, and practically relevant. We aim to produce original and impactful contributions and publish our work in top-tier international venues in artificial intelligence and big data. Please see our Publications for our research outcomes.
LATEST NEWS
LATEST NEWS
[May 2026] Our demo paper has been accepted in IJCAI 2026 (demo track), a top-tier conference in the field of AI.
[May 2026] Our demo paper has been accepted in IJCAI 2026 (demo track), a top-tier conference in the field of AI.
Our demo paper, “Looking at Your Photo, What Comes to Mind? Personalized Memory Internalization for Dementia Reminiscence,” has been accepted to the IJCAI 2026 Demo Track (acceptance rate 26.8%). This system represents a demonstration outcome of our five-year Human-Centered AI project, developed since last year to support memory recall for elderly individuals with dementia. Many thanks to everyone for their hard work, especially to Shunjie for leading the writing, and congratulations to all!
appeared in [News Center (YouTube)] [인하뉴스] [전자신문], etc.
[Apr 2026] Our consortium proposal has been selected for an IITP R&D grant on ultra-efficient lightweight AI.
[Apr 2026] Our consortium proposal has been selected for an IITP R&D grant on ultra-efficient lightweight AI.
Our consortium, led by Polaris Office, has been selected as one of only two funded consortia in an IITP R&D program on lightweight AI training and inference. The project, titled “Development of Ultra-Efficient Lightweight AI Model Technologies Specialized for Document Collaboration toward Digital Sovereignty,” will receive KRW 7.5 billion over four years. Our lab will contribute to the development of efficient AI inference technologies for document collaboration, with a particular focus on task/context-aware reasoning optimization, token pruning, and other lightweight inference techniques.
[Jan 2026] A paper has been accepted in Journal of Artificial Intelligence Research (JAIR), a prestigious journal in the field of AI.
[Jan 2026] A paper has been accepted in Journal of Artificial Intelligence Research (JAIR), a prestigious journal in the field of AI.
Our paper, "PGB: One-Shot Pruning for BERT via Weight Grouping and Permutation ," (1st author: Hyemin Lim, MS alumna) has been accepted for publication in Journal of Artificial Intelligence Research, which is a prestigious SCIE journal (IF 4.6) in artificial intelligence. Well deserved, Hyemin & Jaeyeon!
[Jan 2026] A paper has been accepted in WWW 2026, a top-tier conference (BK 4) in the field of (Web) Data Mining & AI.
[Jan 2026] A paper has been accepted in WWW 2026, a top-tier conference (BK 4) in the field of (Web) Data Mining & AI.
Our paper, "STARK: Structure-Aware and Adaptive Representation Learning for Continual Knowledge Graph Embedding ," (1st author: Kyung-Hwan Lee, MS Student) has been accepted for the WWW 2026 (ACM The Web) conference, which is a top-tier conference in the fields of web-based data mining and AI (BK 4). Congratulations, Kyung-Hwan!
[Nov 2025] A paper has been accepted in ACM SAC 2026, a well-recognized international conference.
[Nov 2025] A paper has been accepted in ACM SAC 2026, a well-recognized international conference.
Our paper, "Balanced Online Class-Incremental Learning via Dual Classifiers ," (1st author: Shunjie Wen, PhD Student) has been accepted for the ACM SAC 2026 conference, which is a reputable international venue in applied computing research (BK 1). Congratulations, Shunjie!
Vacancies
Vacancies
We are always looking for motivated and curious students (BS, MS, and PhD) who are interested in machine learning, data-centric AI, and large-scale models. Please email Prof. Choi your CV, transcript, and (if available) a brief statement of your research interests.
빅데이터 연구실에서는 학부연구생, 석사과정, 박사과정 학생을 모집하고 있습니다. 대학원 진학 및 연구실에 대해서 관심이 있는 학생들은 dchoi@inha.ac.kr로 CV와 간단한 연구(학업)계획서를 첨부해서 이메일을 보내기 바랍니다.
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