CyberC promotes the research areas covered by the following tracks:


Track 1: AI Computing

  • Machine Learning, Deep Learning, Autonomy, and Intelligence
  • Supervised, Unsupervised, and Reinforcement Learning
  • Deep Learning Architectures (CNNs, RNNs, Transformers, GANs)
  • Explainable AI (XAI) and Trustworthy AI
  • Meta-Learning, Few-Shot Learning, and Self-Supervised Learning
  • AI Fairness, Ethics, and Bias Mitigation
  • Large Language Models (LLMs) and Multimodal AI
  • Text Generation, Summarization, and Machine Translation
  • Sentiment Analysis and Opinion Mining
  • Conversational AI and Chatbots
  • Information Retrieval and Knowledge Graphs
  • AI/ML for Robotics & Autonomous Systems
  • AI/ML Applications in Science & Industry
  • AI/ML for Data analytics, social media, and web mining
  • AI for Web computing, intelligent and knowledge based systems

Track 2: Cyber Communications

  • Next-Generation Wireless Networks (5G, 6G, and Beyond)
  • Massive MIMO, Beamforming, and Millimeter-Wave Technologies
  • AI/ML for Wireless Communication and Network Optimization
  • Terahertz (THz) Communication and Free-Space Optics
  • Sensor, IoT Connectivity and Communication Protocols
  • Edge, Fog Computing, and Cloud Computing
  • Low-Power Wide-Area Networks (LPWAN) and Ultra-Reliable Low-Latency Communication (URLLC)
  • AI-Enabled Software/Cognitive Radio Networks
  • Vehicular, UAV, and Satellite Communications
  • Wireless Sensing, Localization, and Positioning
  • Green & Sustainable Wireless Networks
  • Quantum communications, and network computing resources
  • Quantum Computing
  • Spectrum sensing, fusion, decision-making and allocation
  • Signaling process, PHY/link layer protocols and optimization

Track 3: Computer Vision

  • Image Classification, Object Detection, and Segmentation
  • 3D Reconstruction and Scene Understanding
  • Video Analysis and Action Recognition
  • Image and Video Synthesis (GANs, Diffusion Models)
  • Multimodal Learning and Vision-Language Models
  • Transformer Models for Vision (ViTs, Swin, DINO)
  • Self-Supervised and Few-Shot Learning for Vision
  • Explainable AI (XAI) for Vision Applications
  • Adversarial Robustness in Deep Vision Models
  • AI in Medical Imaging and Radiology
  • Image-Guided Surgery and Diagnostics
  • Remote Sensing and Geospatial Vision Applications
  • Vision for Autonomous Vehicles and UAVs
  • SLAM (Simultaneous Localization and Mapping)
  • Human-Robot Interaction and Assistive Vision
  • AI for Image Enhancement and Super-Resolution
  • Deepfake Detection and Synthetic Media Analysis
  • Computational Imaging and Light Field Processing
  • Augmented reality and virtual reality
  • Multispectral, and hyperspectral imaging

Track 4: Security and Privacy

  • Network and System Security
  • AI-Powered Cyber Threat Detection & Response
  • Malware Analysis and Intrusion Detection
  • Zero-Trust Architecture and Adaptive Security
  • Differential Privacy and Federated Learning
  • Secure Multi-Party Computation and Homomorphic Encryption
  • Blockchain and Decentralized Privacy Models
  • Data Anonymization and De-Identification
  • Cryptographic Protocols and Key Management
  • Secure Cloud Computing and Storage
  • Secure Authentication and Access Control
  • Adversarial Machine Learning and AI Robustness
  • AI for Cyber Threat Intelligence and Incident Response
  • Deepfake and Synthetic Media Detection
  • AI Governance, Ethics, and Fairness in Security
  • Quantum Security and Cryptanalysis
  • Privacy in Metaverse and Extended Reality (XR)
  • Secure AI and Privacy-Preserving Generative Models