On-location / Digital Conference

International Conference on Statistical Learning and Artificial Intelligence Applications (ICSL-AIA-27)

02nd - 03rd Feb 2027,New York, USA

In Association With:

Call for Paper


Important Dates


Early Bird Registration

03rd Jan 2027

Paper Submission Deadline

8th January 2027

Registration Deadline

18th January 2027

Conference Date

02nd - 03rd Feb 2027

Conference Updates:

"Stay updated with Science Cite Conference news."

  • Early-Bird Registration Reminder:
    Early-bird registration for the Science Cite Conference in New York ends soon! Register Now!
  • Certificate of Presentation – Recognizing Your Contribution:
    Receive a Certificate of Presentation to recognize your participation in New York conference.
  • Peer Review Process:
    The peer review process will begin soon for New York conference.
  • Networking with Global Experts:
    Join global experts at our conference in New York.
  • Opportunity for Scopus-Indexed Journal Publication:
    Your research could be published in a Scopus-Indexed Journal. Submit Your Abstract
  • SDG-Inspired Conference Focus:
    Present your work aligned with Sustainable Development Goals.

Call For Papers

The ICSL-AIA bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Statistical Learning and Artificial Intelligence Applications, encouraging applied research, case studies, and industry-driven innovations.

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

  • Statistical learning techniques and applications
  • Artificial intelligence in statistical analysis
  • Machine learning methods for data insights
  • Statistical challenges in AI applications
  • Applications of statistical learning in healthcare
  • Predictive modeling using statistical learning
  • Ethics in statistical learning research
  • Statistical software for AI applications
  • Data visualization in statistical learning
  • Statistical methods for deep learning
  • Meta-analysis of statistical learning studies
  • Future trends in statistical learning
  • Collaborative approaches in AI research
  • Statistical learning in social sciences
  • Statistical power in AI studies
  • Applications of statistical learning in finance
  • Statistical methods for natural language processing
  • Data-driven decision making with AI
  • Statistical learning in environmental studies
  • Applications of AI in educational research

Assessment

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Indexed / Supported By

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Academic Institutions Whose Scholars Have Contributed

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