Artificial Intelligence & Mathematical Sciences

Venue:

• Laboratoire d’algorithmique, complexité et logique (LACL), Université Paris-Est Créteil (UPEC), Paris, France

Organisers:

Gaétan Hains (gaetan.hains@u-pec.fr)
Tony Shaska  (shaska@oakland.edu)

Welcome to AIMS2026

We are excited to announce the Artificial Intelligence & Mathematical Sciences (AIMS) 2026 Conference, taking place in Paris, France, from July 7–8, 2026. Hosted by LACL at the Faculté des Sciences et Technologie, UPEC, this event will bring together researchers, academics, and industry professionals to explore the dynamic interplay between artificial intelligence (AI) and the mathematical sciences.

Conference overview

The AIMS 2026 Conference continues the tradition of AIMS meetings as a premier platform for interdisciplinary collaboration at the intersection of AI and mathematics. As AI reshapes data science, cryptography, optimization, and theoretical computer science, its reliance on deep mathematical structures—such as algebraic geometry, number theory, and computational methods—remains crucial.

The conference will showcase cutting-edge research, foster new collaborations, and address fundamental challenges through a mathematical lens.

The conference proceedings will appear in a special journal issue.

Why attend:

Interdisciplinary Scope: Engage with AI and mathematics in an integrated setting.

International Community: Connect with researchers from around the world.

Publication Opportunities: Submit to special issues dedicated to the conference.

Inspiring Venue: Experience Paris while participating in high-level scientific discussions.

Topics

The conference will explore cutting-edge topics at the nexus of AI and mathematical sciences, including:

Data Fabrics and Optimization for AI: Designing mathematical frameworks for data fabrics and optimization algorithms to support AI-driven data integration and processing.

Neurosymbolic Learning and Algebraic Structures: Applying neurosymbolic networks to Galois groups and moduli spaces for polynomial solvability and AI-driven geometric analysis.

Graded Neural Networks and Transformers: Developing graded neural architectures for enhanced AI models, leveraging algebraic structures in machine learning.

Weighted Projective Varieties and Geometric AI: Using weighted hypersurfaces, Finsler metric clustering, and Gröbner bases for computational geometry and data representation in AI.

Rational Points and Zeta Functions in AI Security: Exploring rational points and zeta functions on algebraic varieties for AI-enhanced cryptographic protocols and secure systems.

Special sessions

We are encouraging proposals for special sessions. You can contact the organizers directly if you plan to propose a session.

Program highlights

• Keynote Lectures: Talks from leading experts in AI and mathematics.
• Research Sessions: Peer-reviewed paper presentations.
• Workshops: Hands-on sessions on computational tools and AI applications.
• Panels: Discussions on the future of AI and mathematics.
• Networking Events: Build collaborations within the international AI–Math community.