Dr. RuiDong Qi(祁瑞东)

Research Overview

Our research focuses on cloud computing, big data, service computing, and system development. In cloud computing, we emphasize cloud–edge collaboration, cross-cloud resource scheduling, and task scheduling methods. In big data, we conduct predictive analytics and user behavior analysis within cloud environments, as well as ecosystem service assessment and forecasting. In service computing, we explore advanced recommendation approaches, including retrieval-augmented cold-start, large-model-based, multimodal, and elastic recommendation methods. We also develop system platforms that integrate cloud computing and big data applications, along with intelligent service recommendation systems. At the summit of the cloud, in the ocean of computing power, within the flowing streams of services, and among the vast constellation of big data, we seek those who carry passion and dreams for these frontiers. Let us set sail together, explore the boundless universe of knowledge, and unveil the mysteries of recommender systems. We sincerely invite every undergraduate student with a scientific dream to join our research group, to embark on a poetic journey of research training and project practice. Together, we shall navigate the ocean of knowledge and open up our very own sea of stars. (于云端之巅,算力之海,服务之流,以及大数据的浩瀚星辰之中,我们寻觅着对这些领域充满热情与梦想的你。让我们共同启航,探索知识的无限宇宙,揭开推荐系统的神秘面纱。诚邀每一位怀揣科研梦想的本科生,加入我们的课题组,共同经历科研训练和项目实践的诗意旅程,让我们携手在知识的海洋中航行,开启属于我们的星辰大海。)

Key Research Areas

Cloud Computing

Focus on cloud–edge collaboration, cross-cloud resource scheduling, and task scheduling methods to improve resource utilization and system performance.

Cloud–edge Collaboration
Cross-cloud Resource Scheduling
Task Scheduling
Big Data

Specialize in predictive analytics and user behavior analysis in cloud environments, as well as ecosystem service assessment and forecasting.

Predictive analytics
User behavior analysis
Ecosystem service forecasting
Service Computing

Investigate advanced recommendation methods, including retrieval-augmented cold-start recommendation, large-model-based recommendation, multimodal recommendation, and elastic recommendation.

Cold-start recommendation
Large-model-based Recommendation
Multimodal Recommendation
System Development

Develop system platforms for cloud computing and big data applications, as well as intelligent and efficient service recommendation systems.

Cloud Computing Applications
Big Data Platforms
Intelligent Service Recommendation Systems