Area of research
Computer Networks and Communications · Artificial Intelligence
Research interest
Research interests include IoT and Edge/Fog Computing, Privacy-Preserving Technologies in Data, Age of Information Optimization, and Advanced Neural Network Applications.
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning
V-FedMM: Dynamic sample selection for efficient multimodal federated learning over vehicular networks
Efficient Service Selection and Pricing in Edge-Cloud Computing Markets
Understanding Large Language Models in Your Pockets: Performance Study on COTS Mobile Devices
Laser: Unlocking Layer-Level Scheduling for Efficient Multi-SLO LLM Serving
Energy-Efficient and Dequantization-Free Quantization of LLMs: A Spiking Neural Network Approach to Salient Value Mitigation
LaSen: Low-Altitude Drone Sensing with 5G-NR Signals
Nappa: NNA-Compatible and Privacy-Preserving DNN Training Framework via Vector Decomposition
MicroEdge: An Online Optimization Framework for Cost-Efficient Microservice Orchestration in Edge Native Applications
AIGC-Enhanced Federated Learning: Addressing Data Scarcity in Preference-Based Scenarios
Cetus: Online Context-Aware Cross-Layer Coordination for Efficient Live Volumetric Video Streaming
GraphPilot: GUI Task Automation with One-Step LLM Reasoning Powered by Knowledge Graph
Resource-Efficient Personal Large Language Models Fine-Tuning With Collaborative Edge Computing
Parachute: Dynamic Resource-Aware Privacy-Preserving Video Analytics on Edge
MoEE: Mixture of Edge Experts for Collaborative Inference of Heterogeneous Models Based on Out-of-Distribution Detection
Distributed Cooperative Defense Against DDoS Attacks in Edge-Cloud Computing Networks: A Game-Theoretic Approach
Cooperative and Competitive Pricing in Collaborative Edge Computing
MFEL-H2B: Multimodal Federated Edge Learning with heterogeneity-aware balancing across modalities and models
Joint Bitrate and Resource Adaptation for Super-Resolution Video Streaming in Multi-Cluster Edge Networks: A New Online Learning Approach
CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models
Edge Graph Intelligence: Reciprocally Empowering Edge Networks With Graph Intelligence
Edge Graph Intelligence: Reciprocally Empowering Edge Networks With Graph Intelligence
Delay-Sensitive Task Offloading With Edge Caching Through Martingale-Based Deep Reinforcement Learning
Joint Resource Trading and Task Scheduling in Edge-Cloud Computing Networks
Efficient Coordination of Federated Learning and Inference Offloading at the Edge: A Proactive Optimization Paradigm
Dynamic Edge-Centric Resource Provisioning for Online and Offline Services Co-Location via Reactive and Predictive Approaches
Revisiting Location Privacy in MEC-Enabled Computation Offloading
Online Resource Provisioning and Batch Scheduling for AIoT Inference Serving in an XPU Edge Cloud
Joint Client and Cross-Client Edge Selection for Cost-Efficient Federated Learning of Graph Convolutional Networks
MEC-Enabled Task Replication With Resource Allocation for Reliability-Sensitive Services in 5G mMTC Networks
Fast Situational Awareness and Reliable Response with Heterogeneous Feedback and Number-Theoretic Control Primitives
CAREER: Adding to the Future: Thermal Modeling, Sparse Sensing, and Integrated Controls for Precise and Reliable Powder Bed Fusion
CAREER: Adding to the Future: Thermal Modeling, Sparse Sensing, and Integrated Controls for Precise and Reliable Powder Bed Fusion