ReaLearn (Trustworthy AI Lab)

Welcome !

ReaLearn (Trustworthy AI Lab) is an independent research group, run by Teeradaj Racharak, aimed to address research questions lied in between pure (logic) reasoning and pure (machine) learning for Trustworthy AI including Explainable AI (XAI).

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 anyone who share common interests to join or collaborate with us.

Additional Useful Links

Mentoring plan for prospective and current students
- For some of our poster exhibitions, please visit the Showcase page.

Open Positions

We are looking for RAs / interns and visiting Master / Ph.D. students. Please find more information here.

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)
- Fairness, Accountability, and Transparency (FAccT)
- 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)

June 20, 2025: Our paper "GSR-Fusion: A Deep Multimodal Fusion Architecture for Robust Sign Language Recognition Using RGB, Skeleton, and Graph-based Modalities" is accepted by IEEE Access
May 25, 2025: Our paper "A New Integrated Neurosymbolic Approach for Crop-Yield Prediction Using Environmental Data and Satellite Imagery at Field Scale" is accepted by AI in Geosciences
April 27, 2025: Our paper "Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning" is accepted by NLDB 2025
April 20, 2025: Our paper "Weight-aware Tasks for Evaluating Knowledge Graph Embeddings" is accepted by KBS
March 26, 2025: Our paper "Towards Assumption-based Argumentation Mining in Hotel Reviews" is accepted by CLAR 2025
March 21, 2025: Tongyu-san and Prabhat-san have graduated from the laboratory today. Check out some pictures here.
March 10, 2025: Our paper "Decoder-Focused Knowledge Distillation for Lightweight U-Net Segmentation in Medical Imaging" is accepted by Scientific Reports
March 9, 2025: Our paper "A Strategy for Implementing Garbage Detection in Ontology Completion using Description Logics" is accepted by IEA/AIE 2025
January 23, 2024: We had arranged an interactive seminar on "Argumentation and Its Applications from the Formal View". Please find its details on this page.
January 8, 2025: Our paper "A Framework for Identifying Underspecification in Image Classification Pipeline using Post-hoc Analyzer" is accepted by ICPRAM 2025

For more details, please visit the Events & News page or follow us on Twitter @ReaLearnJaist.

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