01
AI Infrastructure and Systems
Distributed and large-scale training systems for foundation modelsAI-native cloud and data center architecturesReliability, availability, and serviceability of AI systemsObservability for AI infrastructure and systemsHeterogeneous computing for AI (over GPU, TPU, NPU, FPGA, ASIC accelerations)High-performance networking and communication for AI workloadsEfficient inference systems and serving architecturesEdge AI systems and collaborative cloud-edge intelligenceResource management and scheduling for AI clustersAI workload characterization and benchmarkingStorage systems optimized for AI training and data pipelinesMLOps, AIOps, and lifecycle management of AI modelsSecurity, privacy, and trust of AI modelsFederated and distributed AI systemsSystem support for generative AI and large language modelsCo-design of AI algorithms and system architectures
02
RF Computing, Next-Gen AIoT, and Embodied Intelligence
RF Computing & Wireless SensingJoint Communication and Sensing (JCAS) / ISACWiFi, LoRa, and UWB Sensing (Gesture, Vital Signs, Occupancy)Backscatter Communication and Computational RFIDRF-based Localization and Tracking for Autonomous SystemsEnergy Harvesting and Battery-free ComputingReconfigurable Intelligent Surfaces (RIS) for SensingTinyML and Efficient Neural Networks for Edge DevicesDistributed Inference and Learning across IoT ClustersCross-modal Learning (RF + Vision + Audio)Resource-constrained Learning for Embedded SystemsPrivacy-preserving AI in IoT (Federated Learning, Split Learning)Security in IoT systemsRF-Guided Navigation and SLAM (Simultaneous Localization and Mapping)Sim-to-Real Transfer for Wireless RobotsHuman-Robot Interaction via Wearables/RF SensingMulti-agent Coordination in AIoT EnvironmentsSensor Fusion for Embodied Agents (Vision-RF, Lidar-RF)Smart Home, Smart Health, and Smart Factory ApplicationsTestbeds, Datasets, and Evaluation Metrics for RF-AI SystemsHardware-Software Co-design for Sensing and Actuation
03
Web3.0 Security and Privacy
Cryptographic foundations for Web3.0Decentralized protocol securityDecentralized application and smart contract securityDeFi security and economic resilienceGovernance and DAO securityPrivacy-preserving blockchain systemsLayer-2 and cross-chain securitySystem security for decentralized infrastructureTrusted execution and hardware-assisted Web3.0Secure Web3.0 integration in critical industriesWeb3.0 data analytics and forensicsUsability, compliance, and human-centric privacy in Web3.0AI and emerging technologies in Web3.0 security and privacyWeb3.0 measurement and empirical studies
04
Agentic Design for System and Network
LLM-based and foundation model agents for operating system management and automationMulti-agent coordination for distributed computing and parallel workload schedulingAutonomous resource provisioning and orchestration in cloud and edge environmentsAgent-driven fault detection and root cause analysisReinforcement learning agents for adaptive performance optimizationAgentic frameworks for testing, debugging, and program repairIntelligent agents for autonomous network configuration and managementLLM-driven network traffic engineering, routing optimization, and congestion controlMulti-agent systems for software-defined networking and network function virtualizationAutonomous agents for next-generation network environmentsAgent-assisted network protocol design, verification, and simulationLLM-based and agentic approaches for vulnerability discoveryAutonomous agents for intrusion detection and threat huntingAgent-driven malware analysis, reverse engineering, and forensic investigationAdversarial robustness of system-oriented agentsPrivacy and ethical challenges in deploying agents for cybersecurity
05
Edge Intelligence
Edge AI Model Optimization (Quantization, pruning, distillation, compression)On-device deployment of LLMs, vision-language models, and multimodal modelsHigh-efficiency edge SoCs and NPUsHeterogeneous computing architecturesNon-von Neumann paradigms for edge scenariosTask offloading, model partitioning, and collaborative inference strategiesFederated learning and decentralized training for edge networksEdge intelligence agents and their orchestration frameworksPrivacy-preserving edge AISecure aggregation, differential privacy, and zero-trust mechanisms for edge networksDefense against model stealing, data poisoning, and inference-side attacksEdge AI operating systems (Agent OS)Real-time scheduling and resource managementEnergy-efficient computing for battery-powered edge devicesBenchmarks for heterogeneous edge platformsIndustrial IoT and autonomous factoriesAutonomous vehicles and robotic systemsSmart healthcare, smart cities, and consumer electronicsAR/VR with edge intelligence supportPhysical AISemantic communication integrated with edge intelligence6G-edge AI convergenceEdge AI for sustainability and green computing
06
Intelligent Computing
Deep Learning models and applicationsDistributed and Parallel Machine LearningScalable Distributed Training and OptimizationCommunication-efficient Distributed OptimizationFoundation Models and Large-scale training in Distributed EnvironmentsGraph Neural Networks and Large-scale Graph IntelligenceSpatio-temporal and Sequential Learning ModelsFederated and Privacy-preserving Distributed Learning SystemsEdge-Cloud Collaborative Intelligence SystemsAI for High-Performance ComputingIntelligent Resource Scheduling and System OptimizationData-parallel and Model-parallel Computing StrategiesTrustworthy and Robust AI in Distributed SystemsHeterogeneous Computing for Intelligent Workloads (CPU/GPU/TPU)Efficient Distributed Inference and Serving SystemsEnergy-efficient AI in Distributed EnvironmentsIntelligent Computing Applications on Scalable Distributed PlatformsIntelligent computing upgrading traditional industry systems