Special Sessions

At the moment the following special sessions have been confirmed:

Proposals for organizing additional special sessions can be made until Oct. 31, 2025 by contacting Martin Golumbic <golumbic@gmail.com>.

Cooperation, Competition, and Complexity in AI Planning and Learning

Organizers: Alan Kuhnle and Guni Sharon

Strategic interactions are central to many real-world AI systems, from multi-agent planning to reinforcement learning in competitive or cooperative settings. This session explores the interplay between cooperation, competition, and computational complexity in AI planning and learning. The goal is to highlight algorithmic and theoretical advances that deepen our understanding of strategic behavior in sequential decision problems. Topics will include game-theoretic learning dynamics, multi-agent reinforcement learning, submodular coordination, adversarial planning, and complexity-theoretic insights into equilibrium computation and agent interaction.

Data-Driven Decisions: Applied ML and Optimization

Organizers: Munevver Subasi, Ersoy Subasi, and Xianqi Li

This special session brings together researchers and practitioners working at the intersection of optimization and machine learning, emphasizing the growing synergy between these fields in addressing complex, real-world challenges. The session welcomes contributions that explore both theoretical foundations and practical implementations, highlighting advances that enable more efficient, adaptive, and interpretable systems. Areas of interest include novel optimization algorithms, machine learning frameworks, and data-driven decision-making approaches applied to domains such as healthcare, medicine, finance, and other data-intensive fields. By integrating diverse perspectives, this session aims to foster meaningful dialogue, stimulate cross-disciplinary collaboration, and inspire innovative solutions that advance the frontiers of optimization and machine learning in both theory and practice.

AI in Group Theory

Organizers: Elena Bunina and Alexei Miasnikov

This special session explores contemporary Machine Learning and AI techniques as tools for research in group theory and adjacent algebraic/combinatorial domains. We invite talks on AI-assisted conjecture generation and proof search, learning algebraic invariants and representations from data, symbolic–neural pipelines for word/conjugacy/isomorphism problems, and reinforcement or generative methods for algorithm discovery. We also welcome contributions on verification and evaluation (formal proofs, benchmarks, reproducible pipelines). The aim is to bring together mathematicians and AI researchers, showcase concrete case studies (e.g., automorphisms, Burau representations, growth and random groups), and outline open problems and datasets to accelerate progress.

Topics in Math and AI

Organizers: Martin Charles Golumbic and Frederick Hoffman

Five authors of chapters to appear in the forthcoming edited book, Mathematics and Artificial Intelligence (Springer 2026), will present short presentations surveying their contributions.