Workshop Series on Reliable and Interpretable Deep Learning
Challenges, Theories and Applications in Biomedical Informatics
About WS-RIDL 2026
WS-RIDL 2026 gathers researchers at the intersection of AI and biomedical informatics around a shared concern: deep learning systems are increasingly powerful, but deploying them in clinical and scientific settings demands something more. The workshop explores the theory and practice of building models that are robust, transparent, and accountable.
By uniting world-leading scholars from Hong Kong, Mainland China, and the broader international community, WS-RIDL 2026 provides a forum for exchanging ideas on building deep learning systems that are not only powerful but also accountable, auditable, and safe for deployment in biomedical and clinical settings.
Workshop Highlights
Keynote Presentations
Big-picture perspectives on what trustworthy AI demands, from sustainable computing and reliable natural language reasoning to concept-based explainability and genomic foundation models.
View Keynotes →Invited Talks
A diverse set of researchers tackle the hard problems of making deep learning robust and transparent for real-world biomedical applications.
View Invited Talks →Tutorials & Labs
Practical sessions on trustworthy and interpretable deep learning methods, with applications in biomedical informatics.
Explore Tutorials & Labs →Organized By
Sponsored By
