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Call for Papers

Submit your research to Computatia 2027

We invite original, unpublished research papers presenting new results in theory, techniques, or applications of computing technology and artificial intelligence.

Scope

Computatia is an international conference on computing technology and AI, bringing together researchers, academicians, industry practitioners, and policymakers to exchange knowledge on the latest advances in computing and artificial intelligence.

Peer review process

Every submission will undergo a rigorous double-blind peer review by at least two members of the Technical Program Committee. Full details of the review process for 2027 will be published here.

Topics of interest

Submissions are welcome across the following three tracks.

Track 1

Artificial Intelligence & Machine Learning (Core Methods)

  • Data Mining and Knowledge Discovery
  • Big Data Analytics
  • Statistical Learning & Predictive Modelling
  • Time-Series and Spatio-Temporal Data Analysis
  • Multimodal Data Analytics (text, image, audio, video)
  • Deep Neural Network Architectures
  • Convolutional Neural Networks (CNNs) for Vision Tasks
  • Transformer Architectures and Attention Mechanisms
  • Generative AI and Large Language Models (LLMs)
  • Automated Machine Learning (AutoML)
  • Reinforcement Learning for Decision Optimization
  • Graph Analytics, Knowledge Graphs and Ontology-based Reasoning
  • Cognitive Computing and AI-driven Decision Support Systems
  • Conversational AI and Virtual Assistants
Track 2

Computing Systems, Infrastructure & Emerging Technologies

  • Edge AI & Real-time Computing (IoT, Industry 4.0)
  • AI for Cloud Computing & Cloud-Native Platforms
  • Quantum Computing and AI Integration
  • Neuromorphic Computing and Brain-Inspired AI
  • AI for 5G/6G and Beyond Communication Systems
  • AI-powered Edge and Fog Computing
  • Swarm Intelligence and Multi-Agent Systems
  • AI for Cyber-Physical Systems
  • AI for Augmented Reality (AR) and Virtual Reality (VR)
  • High-Performance Computing (HPC) for AI Workloads
  • Distributed and Parallel Computing Architectures for AI
  • AI Accelerators and Hardware (GPUs, TPUs, Neuromorphic Chips)
  • Federated Learning and Distributed AI Systems
Track 3

Security, Privacy & Trustworthy Computing

  • Data Privacy & Security in AI Systems
  • Blockchain and AI for Trustworthy Systems
  • AI for Biometric Security and Identity Verification
  • Adversarial AI and Robustness Research
  • Explainable AI (XAI) and Interpretability
  • Fairness, Bias, and Ethics in AI Systems
  • Differential Privacy and Secure Computation
  • AI for Cybersecurity and Threat Detection
  • Model Verification and Formal Methods for AI Safety
  • Trustworthy and Responsible AI Governance
  • Secure Multi-Party Computation and Cryptographic AI
  • Privacy-Preserving Machine Learning
  • AI Risk Assessment and Auditing