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International Conference on Statistical Learning and Artificial Intelligence - (ICSL-AI-26)

21st - 22nd October 2026 , Washington DC - USA

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Call For Papers

The (ICSL-AI-26) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Statistics,Data Science are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Statistical learning in artificial intelligence
  • Applications of AI in statistical modeling
  • Machine learning techniques for data analysis
  • Statistical methods for predictive modeling
  • Deep learning and statistical inference
  • Statistical challenges in AI research
  • Causal inference in statistical learning
  • Data-driven approaches to AI development
  • Statistical evaluation of machine learning models
  • Feature engineering in statistical learning
  • Bayesian methods in AI applications
  • Statistical frameworks for AI ethics
  • Statistical techniques for big data analysis
  • Unsupervised learning and statistical methods
  • Statistical power analysis in AI studies
  • Reinforcement learning and statistical approaches
  • Statistical tools for AI interpretability
  • Data privacy issues in statistical learning
  • Statistical methods for time series forecasting
  • Statistical education for AI practitioners

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.

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