Lab Overview
Research Directions
Our research integrates computing power systems, service intelligence, and green optimization. We study cloud–edge collaboration, user-centric QoS modeling, and reliable recommendation under practical deployment constraints.
Computing Power Networks & Scheduling: User-perceived and QoS-aware scheduling across heterogeneous hub resources, with cross-domain coordination under operational constraints.
Green & Low-carbon Optimization: Energy-efficient allocation and low-carbon objectives validated through industry collaboration and deployable system prototypes.
Service Computing & Recommendation: Reliable service intelligence—including cold-start, federated, and multimodal recommendation—under data scarcity and privacy needs.
Data-driven Learning & Analytics: Clustering, QoS prediction, and behavioral analytics methods with reproducible protocols and system-level evaluation.
Prospective Students
Students interested in joining the lab should email a concise self-introduction and academic background. We prioritize candidates with clear research interests and solid foundations in systems, networking, or machine learning.
- •Your CV, transcripts (if available), and 1–2 related projects.
- •Why this lab, and how your experience connects to our research themes.
- •Any constraints (schedule, language preference, and expected start time).
- •Contact: imucsrdq@163.com
Principal Investigator
Principal Investigator
Dr. RuiDong Qi（祁瑞东）
Principal Investigator in Computing Power Networks | Green AI and Service Intelligence
College of Computer Science, Inner Mongolia University
Our research integrates computing power systems, service intelligence, and green optimization. We study cloud–edge collaboration, user-centric QoS modeling, and reliable recommendation under practical deployment constraints.
Graduate Students
ZhiBo Zhang（张志博）
Masters Student
Interests: Multi-Objective Optimization, Task Scheduling, Computing Power Networks
Develops multi-objective models and adaptive scheduling algorithms for distributed heterogeneous computing, using heuristic and ML methods to reduce delay and energy use in low-carbon infrastructures.
Lei Yuan（袁磊）
Masters Student
Interests: Task scheduling, Generative Models, Computing Power Networks
Studies intelligent scheduling for heterogeneous computing with ML and reinforcement learning, optimizing allocation, throughput, and latency under real-time QoS constraints.
XueMei Deng（邓雪梅）
Masters Student
Interests: Cloud-Edge-Device Collaborative Inference, Computing Power Networks
Designs adaptive scheduling policies for distributed heterogeneous systems, combining reinforcement learning and generative methods to balance latency and energy efficiency.
Ao Sun（孙奥）
Masters Student
Interests: Cold Start Problem, Service Recommendation, Computing Power Networks
Works on cold-start cloud service recommendation using meta-learning and graph neural networks to improve performance for new services and users under data scarcity.
MingJie Wu（吴明杰）
Masters Student
Interests: Recommendation system, LLM, Service recommendation, Computing Power Networks
Investigates service recommendation enhanced by large language models, focusing on practical deployment and robustness under sparse interaction data.
YunShen Zhao（赵昀森）
Masters Student
Interests: Multimodal Fusion, Power Forecasting
Builds spatiotemporal forecasting frameworks for green power by fusing NWP, satellite, and ground observations with deep learning for grid stability and renewable integration.
LanLan Yang（杨兰兰）
Masters Student
Interests: Green power Scheduling, Data Analysis, Computing Power Networks
Studies online job scheduling and deep reinforcement learning for low-carbon data-center operation and energy-aware resource management.
LiJun Dong（董利军）
Masters Student
Interests: Task Scheduling, Computing Power Networks
Focuses on task scheduling to improve resource utilization and low-carbon power usage in data centers, with emphasis on reproducible system evaluation.
PengHui Feng（冯鹏辉）
Masters Student
Interests: Task Scheduling, Computing Power Networks
Develops edge collaborative scheduling with feedback-diffusion models, balancing efficiency, latency, and robustness in heterogeneous environments.
Hao Ma（马浩）
Masters Student
Interests: Task Offloading, Task Recommendation, Computing Power Networks
Researches task offloading and recommendation in computing power networks to improve allocation efficiency under operational constraints.
RuoShen Jia（贾若森）
Masters Student
Interests: Recommendation system, Task Recommendation, Computing Power Networks
Optimizes machine-learning recommendation algorithms for service and task recommendation, aiming for stronger accuracy and user experience under sparse data.
Undergraduate Researchers
WenBin Zhao（赵文斌）
College Student
Interests: Edge-Cloud Computing, Task Scheduling, Reinforcement Learning
Works on resource optimization and task scheduling in cloud–edge systems using reinforcement learning and heuristics to balance compute, latency, and service quality.
FuCheng Zhang（张甫丞）
College Student
Interests: Cross-Domain Scheduling, Resource Optimization, Computing Force Network
Studies computing-force network architecture and cross-domain scheduling for coordinated allocation across heterogeneous domains.
Yue Liu（刘岳）
College Student
Interests: Computing Force Network, Clustering Algorithms, Distributed Machine Learning
Improves clustering algorithms in computing-force networks for scalability and robustness on complex, distributed data.
YuDa Cheng（程宇达）
College Student
Interests: Computing Force Network, Clustering Algorithms, Unsupervised Learning
Focuses on clustering and unsupervised learning in computing-force networks with emphasis on algorithm optimization and data-analysis applications.
JingHe Tian（田敬赫）
College Student
Interests: Cold Start Problem, Service Recommendation, Computing Power Networks
Develops cold-start recommendation with multimodal and contextual features, plus lightweight models for high-concurrency deployment.
WenBo Xue（薛文博）
College Student
Interests: Service Recommendation, Computing Power Networks
Combines user-behavior analysis with data-driven methods to build scalable service recommendation systems.
ShouTing Fan（樊首廷）
College Student
Interests: Service Recommendation, Computing Power Networks
Architects AI-driven recommendation platforms that integrate service design with deployable learning pipelines.
PengFei liu（刘鹏飞）
College Student
Interests: Service Recommendation, Computing Power Networks
Applies big-data processing and recommendation algorithms to improve quality and efficiency in service computing systems.
Shuo liu（刘硕）
College Student
Interests: Computing Power Networks
Works on software performance optimization and computational efficiency for data-intensive workloads.
Lab Alumni
CongRong Wu（吴从荣）
Former Master student (Graduated 2026)
Focus: Task Scheduling for Computing Power Networks
Now: Technology Position @ Bank of China Shandong Branch
Undergraduate Alumni
Le Yang（杨乐）
Former Undergraduate student (Graduated 2026)
Focus: Clustering
Now: Study for a Doctorate @ The Elite Program of Inner Mongolia University（内蒙古大学）
YueQi Wang（王玥祁）
Former Undergraduate student (Graduated 2026)
Focus: Clustering
Now: Study for a Master’s Degree @ Northeastern University （东北大学）
YiXuan Dai（戴轶轩）
Former Undergraduate student (Graduated 2026)
Focus: Clustering
Now: Study for a Master’s Degree @ University of Science and Technology of China（中国科学技术大学）