13^th International Conference on Foundations of Computer Science & Artificial Intelligence (FCSAI 2026)

August 15 ~ 16, 2026,Melbourne, Australia

Scope & Topics

13^th International Conference on Foundations of Computer Science & Artificial Intelligence (FCSAI 2026)is a premier global forum dedicated to advancing the theoretical, methodological, and foundational aspects of Computer Science and Artificial Intelligence. Building on the legacy of the CST conference series, FCSAI 2026 expands its scope to embrace the rapidly evolving landscape of AI, machine learning, and intelligent systems, while maintaining a strong commitment to the core principles of theoretical computer science.

The goal of FCSAI 2026 is to bring together researchers, practitioners, and innovators from academia and industry to deepen the understanding of modern computational foundations and intelligent technologies. The conference aims to foster collaboration across disciplines, promote rigorous scientific exchange, and highlight cutting edge developments that shape the future of computing and AI.

Authors are invited to contribute high quality research articles, case studies, survey papers, and industrial experiences that demonstrate significant advances in foundational computer science, artificial intelligence theory, and their emerging intersections. Submissions may address, but are not limited to, the topics listed below.

Topics of interest include, but are not limited to, the following

Algorithms, Complexity and Optimization Theory


• Algorithms and Data Structures
  
• Approximation Algorithms and Hardness of Approximation
  
• Fine Grained Complexity
  
• Computational Complexity Theory
  
• Communication and Information Complexity
  
• Randomized and Probabilistic Algorithms
  
• Streaming and Sublinear Time Algorithms
  
• Dynamic Algorithms and Data Structures
  
• Modern Graph Algorithms (connectivity, flows, cuts, near linear time)
  
• Discrepancy Theory and Combinatorial Optimization
  
• Spectral Graph Theory
  
• High Dimensional Probability in Algorithms
  
• Beyond Worst Case Analysis
  
• Smoothed Analysis
  
• Algebraic and Symbolic Computation
  
• Computational Geometry
  
• Large Scale and Uncertainty Aware Optimization
  
• Energy Efficient and Resource Aware Algorithms
  
• Algorithmic Foundations of AI (search, planning, heuristics)
  
• Computational Models of Intelligent Behavior
  
  Theoretical Artificial Intelligence
  
  
• Foundations of Artificial Intelligence
  
• Computational Models of Intelligence
  
• Multi Agent Systems and Game Theoretic AI
  
• Planning, Search and Decision Making Theory
  
• Knowledge Representation and Reasoning
  
• Logic Based AI
  
• Neurosymbolic AI Theory
  
• AI Alignment and Safety Theory
  
• Interpretability and Explainability Theory
  
• Computational Cognitive Science
  
• Theory of Intelligent Agents
  
• Symbolic, Subsymbolic and Hybrid AI Models
  
  Machine Learning Theory, Optimization and Foundations of AI
  
  
• Computational Learning Theory
  
• Statistical Learning Theory
  
• Optimization Theory for ML
  
• Implicit Bias of Gradient Descent
  
• Generalization in Overparameterized Models
  
• Computational Statistical Tradeoffs
  
• Reinforcement Learning Theory
  
• Theory of Foundation Models and Large Scale Neural Networks
  
• Adversarial Learning and Robustness
  
• Algorithmic Fairness and Transparency
  
• High Dimensional Geometry for ML
  
• Causal Inference Theory
  
• Clustering Theory and High Dimensional Data Analysis
  
• Explainability, Interpretability and Ethical Foundations of AI
  
• Theoretical Guarantees for Large Language Models
  
• Learning Dynamics and Emergent Behaviors in AI Systems
  
  Quantum Computing and Quantum Information Theory
  
  
• Quantum Algorithms and Quantum Advantage
  
• Quantum Complexity Theory
  
• Quantum Error Correction and Fault Tolerance
  
• Hamiltonian Complexity
  
• Quantum Simulation Algorithms
  
• Quantum Machine Learning Theory
  
• Quantum Cryptography and Cryptanalysis
  
• Quantum Information Theory
  
  Cryptography, Security and Information Theory
  
  
• Cryptography (Classical and Post Quantum)
  
• Lattice Based Cryptography
  
• Secure Multiparty Computation
  
• Zero Knowledge Proofs and Interactive Proofs
  
• Pseudorandomness and Derandomization
  
• Coding Theory and Error Correcting Codes
  
• Differential Privacy Theory
  
• Functional Encryption and Advanced Primitives
  
• Information Theoretic Security
  
• Blockchain and Consensus Theory
  
• Cryptography from Worst Case Assumptions
  
• Theoretical Cryptanalysis (including quantum)
  
• Security Foundations for AI Systems
  
  Logic, Semantics, Verification and Automated Reasoning
  
  
• Program Semantics and Verification
  
• Formal Methods and Model Checking
  
• Type Theory and Proof Theory
  
• SAT/SMT Solving Theory
  
• Proof Complexity
  
• Automated Reasoning
  
• Symbolic Execution Theory
  
• Formal Verification of ML Systems
  
• Verified AI and Safety Critical Reasoning
  
• Logical Foundations of AI Planning and Inference
  
  Distributed, Parallel and Networked Computation
  
  
• Distributed Algorithms and Complexity
  
• Parallel Computing Theory
  
• Local Algorithms and Graph Models
  
• Congested Clique and Modern Distributed Models
  
• Fault Tolerant Distributed Systems
  
• Consensus and Synchronization
  
• Distributed Lower Bounds
  
• Distributed Optimization
  
• Federated Learning Theory
  
• Dynamic Graph Algorithms
  
• Algorithmic Foundations of Cloud–Edge Systems
  
• Distributed AI and Multi Agent Coordination
  
  Interdisciplinary and Computational Sciences
  
  
• Theory applied to natural sciences, social systems and economics.
  
• Algorithmic Game Theory and Mechanism Design
  
• Computational Economics
  
• Computational Social Science
  
• High Dimensional Geometry and Metric Embeddings
  
• Statistical Physics Methods in Algorithms
  
• Computational Biology (theoretical foundations)
  
• Computational Chemistry and Physics (complexity theoretic framing)
  
• Foundations of Data Science
  
• Ethical and Societal Aspects of Algorithms
  
• Energy Aware and Sustainability Driven Computation
  
• AI for Scientific Discovery (theoretical foundations)
  

Paper Submission

Authors are invited to submit papers through the conference Submission System by May 30, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).

Selected papers from CST 2026, after further revisions, will be published in the special issue of the following journals.

• International Journal of Computer Science & Information Technology (IJCSIT)
  
• International Journal of Information Technology Convergence and Services (IJITCS)
  
• International Journal of Computer Science, Engineering and Applications (IJCSEA)
  
• International Journal in Foundations of Computer Science & Technology (IJFCST)
  
• Information Technology in Industry (ITII)
  

Important Dates

Co - Located Event

• 5^th International Conference on Software Engineering and Automation (SEAU 2026)
• 15^th International Conference on Information Technology Convergence and Services (ITCS 2026)
  
• 7^th International Conference on VLSI & Embedded Systems (VLSIE 2026)
  
• 5^th International Conference on NLP and Machine Learning Trends (NLMLT 2026)
  
• 17^th International Conference on Database Management Systems (DMS 2026)
  
• 7^th International Conference on Cloud and Big Data (CLBD 2026)
  
• 7^th International Conference on Networks & IOT (NeTIOT 2026)
  

***** The invited talk proposals can be submitted to cst@cst2026.org