Current Team Members
CongRong Wu（吴从荣）
Masters Student
Interests: Time Series Prediction, Reinforcement Learning, Task Scheduling, Computing Power Networks
Current research is shifting from cloud computing towards the establishment of a new computing-power network paradigm, focusing on enhancing the intelligent scheduling capability of the Computing Network Brain to improve the utilization efficiency of global computing resources.
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. Leverages heuristic and ML methods to minimize execution delay and energy consumption, enabling low-carbon computing infrastructure.
Lei Yuan（袁磊）
Masters Student
Interests: Task scheduling, Generative Models, Computing Power Networks
Develops intelligent scheduling for heterogeneous computing using ML/RL to optimize task allocation, throughput, and latency under real-time and QoS constraints. Enhances resource efficiency for generative workloads and low-carbon infrastructure.
XueMei Deng（邓雪梅）
Masters Student
Interests: Cloud-Edge-Device Collaborative Inference, Computing Power Networks
Designs intelligent adaptive scheduling policies for distributed heterogeneous computing. Leverages reinforcement learning and generative algorithms to dynamically optimize task allocation, balancing low latency and energy efficiency for sustainable computing infrastructure.
Ao Sun（孙奥）
Masters Student
Interests: Cold Start Problem, Service Recommendation, Computing Power Networks
My research goal is to optimize the cold-start of cloud service recommendation through meta-learning, graph neural networks and various technologies under data scarcity, so as to improve the recommendation performance for new services/users.
MingJie Wu（吴明杰）
Masters Student
Interests: Recommendation system, LLM, Service recommendation, Computing Power Networks
Wu Mingjie is currently researching the content related to service recommendation, especially how to use large language models to enhance the performance of service recommendation.
YunShen Zhao（赵昀森）
Masters Student
Interests: Multimodal Fusion, Power Forecasting
Develops spatiotemporal forecasting frameworks for green power by fusing multi-source data (NWP, satellite, ground observations) with deep learning to enhance grid stability and renewable energy integration.
LanLan Yang（杨兰兰）
Masters Student
Interests: Green power Scheduling, Data Analysis, Computing Power Networks
Online Job Scheduling for Low-Carbon Data Center Operation，Low-Carbon Operation of Resources Based on Deep Reinforcement Learning
LiJun Dong（董利军）
Masters Student
Interests: Task Scheduling, Computing Power Networks
Focus on using task scheduling methods to improve the utilization of resources and green power in data centers. A reader immersed in a fantasy world and a amateur writers who do not write most of the time.
PengHui Feng（冯鹏辉）
Masters Student
Interests: Task Scheduling, Computing Power Networks
Addressing the challenges of edge collaborative algorithms through a feedback-diffusion model scheduling method. Spending time in the virtual world to relax both mentally and physically.
Hao Ma（马浩）
Masters Student
Interests: Task Offloading, Task Recommendation, Computing Power Networks
My research focuses on task offloading and recommendation in computing power networks, aiming to optimize resource allocation and enhance service efficiency. Reading for cozy stories, gaming for epic wins! Swap books for controllers after dark—my perfect kind of fun.
RuoShen Jia（贾若森）
Masters Student
Interests: Recommendation system, Task Recommendation, Computing Power Networks
My research optimizes machine learning recommendation algorithms to boost performance and user experience, advancing intelligent recommendation. Focused on machine learning recommendation algorithms to uncover user needs and offer personalized recommendations.
Undergraduate Student
Le Yang（杨乐）
College Student
Interests: Clustering, Multimodal Sentiment Analysis,, Big Data Processing
Computer Science undergraduate with strong academic record and research experience. First-author paper at CCF-C conference on clustering algorithms; led provincial project on multimodal depression prediction. Awarded multiple national and provincial competition awards and scholarships. Demonstrated leadership as Vice Secretary of the College Youth League Committee. Proficient in English technical reading.
YueQi Wang（王玥祁）
College Student
Interests: Artificial Intelligence, Data Intelligence,, Interpretable Machine Learning
Focuses on AI and data intelligence applications, specializing in optimized clustering algorithms, deep learning for medical data processing, and intelligent analytics in education. Committed to enhancing interpretability and cross-domain applications of intelligent algorithms, aiming to drive theoretical innovation and practical impact in healthcare, education, and smart society.
WenBin Zhao（赵文斌）
College Student
Interests: Edge-Cloud Computing, Tasking Scheduling, Reinforcement Learning
Research focuses on resource optimization and task scheduling in cloud-edge computing systems. Develops dynamic scheduling models using reinforcement learning and heuristic algorithms to balance computing power and latency, addressing challenges such as task blocking, high energy consumption, and unstable service quality in multi-device environments.
FuCheng Zhang（张甫丞）
College Student
Interests: Cross-Domain Scheduling, Resource Optimization, Computing Force Network
Research focuses on computing force network architecture and cross-domain resource scheduling. Develops intelligent scheduling mechanisms to enable efficient and coordinated allocation of distributed computing resources across heterogeneous domains, optimizing overall network utilization and service performance.
Yue Liu（刘岳）
College Student
Interests: Computing Force Network, Clustering Algorithms, Distributed Machine Learning
Research focuses on optimizing and innovating clustering algorithms in computing force networks to enhance their performance, efficiency, and robustness on complex data. Explores the integration of machine learning, data analysis, and cloud computing technologies to develop scalable and adaptive clustering solutions for distributed and data-intensive environments.
YuDa Cheng（程宇达）
College Student
Interests: Computing Force Network, Clustering Algorithms, Unsupervised Learning
Research focuses on clustering algorithms and unsupervised learning in computing force networks, with an emphasis on algorithm optimization and practical applications in data analysis. Explores innovative approaches to enhance the performance and adaptability of clustering methods in complex, real-world data scenarios.
JingHe Tian（田敬赫）
College Student
Interests: Cold Start Problem, Service Recommendation, Computing Power Networks
Research focuses on cold-start recommendation systems, integrating multimodal features (text/image) and contextual information to reduce dependency on traditional interaction data. Develops personalized strategies for long-tail items and new users, while optimizing lightweight model architectures for efficient deployment in high-concurrency scenarios.
WenBo Xue（薛文博）
College Student
Interests: Service Recommendation, Computing Power Networks
Specializes in user demand analysis and intelligent recommendation. Utilizes qualitative/quantitative research and data-driven strategies to deeply understand user behavior, building accurate and scalable recommendation systems that enhance product experience and business outcomes.
ShouTing Fan（樊首廷）
College Student
Interests: Service Recommendation, Computing Power Networks
Specializes in architecting and implementing AI-driven recommendation platforms. Combines expertise in service design and artificial intelligence to build scalable systems that deliver precise, personalized recommendations and create tangible business value.
PengFei liu（刘鹏飞）
College Student
Interests: Service Recommendation, Computing Power Networks
Research focuses on big data technologies and their applications in service recommendation systems. Developing innovative recommendation algorithms and data processing frameworks to improve recommendation quality and system performance.
Shuo liu（刘硕）
College Student
Interests: Computing Power Networks
Research focuses on software performance optimization and computational efficiency. Designing innovative approaches to accelerate code execution, reduce computational overhead, and enhance overall system performance through advanced optimization techniques.