▪ Generative AI and foundation models (LLMs, diffusion models)
▪ Explainable, fair, and responsible AI
▪ Reinforcement learning and multi-agent systems
▪ AI for science (healthcare, climate, material discovery)
▪ TinyML, on-device learning, and edge intelligence
▪ Robustness, adversarial learning, and uncertainty quantification
▪ Neural architecture search and model compression
▪ AI agents, planning, and tool-augmented reasoning

▪ Large-scale data processing and cloud analytics
▪ Graph mining, knowledge graphs, and graph neural networks
▪ Data quality, data lineage, and data governance
▪ Time-series, spatio-temporal, and streaming analytics
▪ Privacy-preserving data sharing (differential privacy, synthetic data)
▪ Automated machine learning (AutoML) and feature engineering
▪ Multimodal data fusion (text, image, sensor, graph)
▪ Data systems for LLM training, retrieval, and fine-tuning

▪ Vision-language models and multimodal understanding
▪ 3D vision, novel view synthesis (NeRF, 3D Gaussian Splatting)
▪ Video understanding, action recognition, and generation
▪ Medical image analysis and computational pathology
▪ Real-time perception for autonomous systems
▪ Generative models for image editing and restoration
▪ Few-shot, self-supervised, and open-world recognition
▪ Vision for AR/VR, robotics, and embodied AI

▪ 6G, beyond 5G, and integrated sensing and communication
▪ Edge computing, serverless, and cloud-edge continuum
▪ Blockchain, smart contracts, and Web3 security
▪ Zero trust architecture and identity-centric security
▪ Cyber resilience, attack detection, and automated response
▪ Post-quantum and quantum-safe cryptography
▪ IoT/IIoT security and lightweight protocols
▪ Privacy-enhancing technologies (PETs) and anonymous networks

▪ AI-assisted software development (code generation, testing, debugging)
▪ DevOps, MLOps, LLMOps, and infrastructure as code
▪ Software quality, technical debt, and maintainability
▪ Human-centric, accessible, and sustainable software
▪ Digital twins, low-code/no-code platforms, and metaverse systems
▪ Requirements engineering for AI and data-centric systems
▪ Enterprise architecture, microservices, and API ecosystems
▪ Empirical software engineering and mining software repositories

▪ Computational biology, genomics, and single-cell analysis
▪ AI for energy systems, smart grids, and climate modeling
▪ Human-computer interaction, affective computing, and neuro-symbolic interfaces
▪ Computational social science and social network analysis
▪ Green computing, energy-efficient AI, and sustainable IT
▪ AI ethics, policy, and governance of autonomous systems
▪ Digital health, wearable computing, and remote patient monitoring
▪ CS education, MOOCs, and AI tutors