Welcome !
ReaLearn (Trustworthy AI Lab) is a research laboratory aimed to address research questions lied in between pure (logic) reasoning and pure (machine) learning for Trustworthy AI including Explainable AI (XAI). This lab is run by Teeradaj Racharak (X) at Japan Advanced Institute of Science and Technology (JAIST) and is also working collaboratively with Nguyen lab of JAIST, which conducts research on Natural Language Processing (NLP) and Deep Learning.
At ReaLearn, our study covers two mainstreams of AI, namely, (1) Knowledge Representation and Reasoning (KRR) and (2) Machine Learning (ML). Our ultimate goal is to build AI systems that humans can trust! We welcome any students who share common interests to join or collaborate with us.
Additional Useful Links
Mentoring plan for prospective and current students
ReaLearn Lab introductory slide
For some of our poster exhibitions, please visit the Showcase page.
Research Topics
KRR for Trustworthy AI, e.g.,
Description Logic and Explanation for Ontology and Knowledge Graph applications
Argumentation and Explanation for Practical Reasoning
Human-friendly Explanation Formalisms
Implementation of XAI system, e.g., Virtual Knowledge Graph
Integration of KRR and ML for Trustworthy AI, e.g.,
Knowledge Graph Embedding (i.e. Knowledge Graph meets ML)
Ontology Embedding (i.e. Ontology meets ML)
Neural-symbolic Learning (i.e. Symbolic Reasoning meets ML)
Argument Mining (i.e. Argumentation meets ML)
Knowledge-aware Machine Learning
Any topics involved the development of Trustable AI systems are always welcome!
For more details, please visit the Research page.
News and Highlights
(For all events in the past, see this page)April 18, 2024: Our paper "DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation" is accepted by Frontiers in Bioengineering and Biotechnology
April 2, 2024: Racharak has become a committee member of ISO on Artificial Intelligence.
March 22, 2024: Sotaro Shimizu-san has graduated from the laboratory today. Check out some pictures here.
March 19, 2024: Racharak has received the National Research Awards 2024 from National Research Council of Thailand
December 30, 2023: Our paper "A Quantitative Assessment Framework for Modelling and Evaluation using Representation Learning in Smart Agriculture Ontology" is accepted by ICAART 2024
December 27, 2023: We had arranged the winter camp (brown bag) 2023. Please find its details on this page.
September 19, 2023: Racharak has been recognized as an IEEE CertifAIEd Authorized Lead Assessor.
For more details, please visit the Events & News page or follow us on Twitter @ReaLearnJaist.