Computational Social Science: Simulation, prediction, and modeling of social phenomena and communities
Human-AI Collaboration Patterns: Workflow design for AI agent and human cooperation
Social Network Dynamics: Behavioral pattern mining and analysis in large-scale social networks
Collective Intelligence: Group decision-making, crowdsourcing, and distributed problem-solving systems
Agent-Driven Social Modeling: Using AI agents for social behavior analysis and human preference learning
Social Media and Cultural Computing: Content analysis, sentiment analysis, cross-cultural AI systems, and bias mitigation
AI Ethics and Social Impact: Research on AI systems' effects on social structures, relationships, and equity
Explainable and Socially-Aware AI: Interpretable AI systems that understand and adapt to social contexts
Digital Governance and Policy: AI applications in public administration, policy-making, and social governance
Social Robotics and Interaction: Human-robot interaction in social and collaborative contexts
Digital Humanities: AI applications in humanities research and cultural heritage preservation

Visual Foundation Models & Self-Supervised Learning: Large-scale pre-training (CLIP, DINO, MAE), vision transformers, multi-modal alignment, universal visual representations
Generative Vision & Content Creation: Diffusion models for image/video/3D generation, neural rendering (NeRF, Gaussian Splatting), style transfer, AI-powered artistic tools
Video Understanding and Temporal Modeling: Action recognition, video captioning, slow/fast motion analysis, long-form video understanding, spatio-temporal transformers
Low-Level Vision and Computational Imaging: Image restoration (super-resolution, denoising, inpainting), HDR imaging, medical/remote sensing image analysis, novel camera/sensor models
3D Vision and Geometric Learning: 3D reconstruction from images/videos, point cloud processing, scene understanding for robotics/autonomous driving, SLAM with deep learning
Efficient and Real-Time Vision Systems: Model compression for edge devices, neural architecture search for vision, mobile/embedded vision applications, green AI in vision
Vision for Embodied AI and Robotics: Visual navigation, manipulation from visual feedback, human-robot interaction via vision, simulation-to-real transfer
Vision-Language Integration: Vision-language models (VLMs), visual question answering, image/video captioning, text-to-image generation and editing
Trustworthy and Explainable Computer Vision: Adversarial robustness, fairness and bias mitigation in vision models, interpretability (saliency maps, concept vectors), uncertainty quantification.
Emerging Applications and Cross-Domain Vision: AI for scientific discovery (microscopy, astronomy), vision in AR/VR/Metaverse, agricultural vision, sports analytics, wildlife monitoring

Efficient and Scalable Language Models: Efficient Transformer architectures, model compression (pruning, quantization), distributed training, low-resource/few-shot learning for NLP
Natural Language Understanding and Reasoning: Semantic parsing, logical reasoning, commonsense knowledge integration, fact verification, complex question answering over texts/knowledge graphs
Natural Language Generation and Dialogue: Controllable and safe text generation, open-domain & task-oriented dialogue systems, storytelling, personalized content creation
Multilingual and Cross-Lingual NLP: Machine translation for low-resource languages, cross-lingual transfer learning, linguistic diversity in LLMs, dialect and code-switching handling
Speech, Audio, and Multimodal Integration: End-to-end speech processing, text-to-speech synthesis, audio event detection, audio-visual-language models for holistic understanding
Information Retrieval and Knowledge-Intensive NLP: Dense retrieval, retrieval-augmented generation (RAG), knowledge base population and completion, factuality and hallucination mitigation
NLP for Scientific and Professional Domains: Biomedical/clinical text mining, legal document analysis, financial sentiment and risk prediction, scientific literature discovery (AI4Science)
Socially-Aware and Human-Centered NLP: Social media analysis for public health/safety, misinformation detection, computational social science, cultural bias analysis and mitigation
Interpretability, Analysis, and Evaluation of Language Models: Mechanistic interpretability, probing, benchmarking beyond accuracy (e.g., robustness, fairness), new evaluation paradigms for generative tasks
Emerging Applications and Human-AI Collaboration: AI assistants for education and creativity, programming with natural language (Code LLMs), NLP for accessibility, human-in-the-loop text processing systems

Machine Learning for Scientific Discovery & Engineering Design: ML-driven material discovery and property prediction, generative models for molecular and protein design, AI-assisted chip/mechanical design automation
Machine Learning in Smart Infrastructure & Construction: Predictive maintenance of bridges and power grids using sensor data, computer vision for construction site safety and progress monitoring, resource optimization in logistics
Machine Learning for Advanced Manufacturing & Industry 4.0: ML-based quality control and defect detection, predictive maintenance for industrial equipment, digital twin simulation and optimization, smart supply chain management
Machine Learning in Energy Systems & Sustainability: ML for smart grid load forecasting and renewable energy integration, optimization of carbon capture processes, battery health prediction and management, climate modeling downscaling
Machine Learning for Autonomous Systems & Robotics: Perception, planning, and control algorithms for autonomous vehicles and drones, reinforcement learning for robotic manipulation and navigation, simulation-to-real transfer in robotics
Machine Learning in Computational Biology & Digital Health: ML for genomics and drug discovery, predictive diagnostics from medical images and EHR data, wearable sensor data analysis for personalized health monitoring
Machine Learning for Financial Technology & Quantitative Engineering: Algorithmic trading models, ML-based fraud detection and risk assessment, credit scoring using alternative data, blockchain data analysis and DeFi applications
Machine Learning in Environmental Science & Earth Observation: Satellite imagery analysis for disaster monitoring and agriculture, ML models for weather and climate forecasting, biodiversity monitoring and ecological modeling
Edge AI & Distributed Machine Learning for Engineering Systems: TinyML for embedded and IoT devices, federated learning for privacy-preserving industrial data collaboration, distributed optimization in sensor networks
Trustworthy Machine Learning for Safety-Critical Engineering: Robustness and safety verification of ML models in engineering contexts, interpretable ML for high-stakes decision support, uncertainty quantification in engineering predictions

AI/ML Hardware Accelerators & Architecture: Domain-specific architectures (GPUs, TPUs, NPUs), in-memory computing, neuromorphic chips, photonic computing for ML
Model Compression & Efficient Inference: Pruning, quantization, knowledge distillation, neural architecture search for edge devices, low-latency serving systems
Compiler & Framework Optimizations: ML compilers (TVM, MLIR), graph optimization, automatic kernel fusion, cross-platform deployment tools
Distributed & Federated Learning Systems: Large-scale training systems, communication-efficient algorithms, privacy-preserving federated learning frameworks, heterogeneous device management
ML for System Optimization & Autotuning: ML-based compiler heuristics, auto-tuning database parameters, AI-driven resource scheduling in data centers
Sustainable AI (Green Computing): Energy-aware training algorithms, carbon footprint measurement and optimization of ML workloads, cooling and power management