邮箱：liuyaoqi@ict.ac.cn
简介：
刘垚圻，副研究员，深理工算力微电子学院-中科天算天基计算硕士班导师，中科天算董事长。2022年博士毕业于中国科学院大学，入选计算所”新百星计划“。现任中国计算机学会容错计算专委副秘书长、执行委员，中国通信学会数字孪生与系统仿真专委秘书、执行委员。主持或参与多项重大面向航天应用的高性能高可靠容错计算、大规模星座协同计算架构、天地一体化信息网络关键技术研究项目，包括北京市自然科学基金及其重点项目、中国科学院战略优先研究项目、国家重点研发计划等，曾担任多个项目的首席研究员或执行领导。在计算机体系结构和航天信息系统领域有丰富的经验，研究方向聚焦于天基计算领域，发表论文40余篇，发明型专利60余项。
研究方向：
（1）天基计算体系结构；
（2）天基大模型与智能应用；
（3）计算与通信、遥感融合；
现面向具有强烈科研热情的优秀人才（硕士生及实习生）开放招募！
简历投递：liuyaoqi@ict.ac.cn
Liu Yaoqi, associate researcher, tutor of Tiansuan Space-based Computing Master Program of Shenzhen Institute of Computing and Microelectronics, and chairman of Tiansuan. In 2022, he will graduate from the University of Chinese Academy of Sciences and be selected into the "New Hundred Stars Plan" of the Institute of Computing Science. He is currently the deputy Secretary-General and executive member of the Fault Tolerant Computing Committee of China Computer Society, and the secretary and executive member of the Digital Twin and System Simulation Committee of China Communications Society. Presided over or participated in a number of important research projects on high performance, high reliability and fault-tolerant computing, large-scale constellation collaborative computing architecture, and key technologies of space-ground integration information network for aerospace applications, including Beijing Natural Science Foundation and its key projects, strategic priority research projects of Chinese Academy of Sciences, and national key research and development plans, etc., and served as principal researcher or executive leader of several projects. He has rich experience in the field of computer architecture and aerospace information systems, and his research direction focuses on space-based computing. He has published more than 40 papers and more than 60 invention patents.
Research Interests:
（1）Space-Based Large Model and Intelligent Applications
Space-based large model systems aim to deploy and execute advanced AI models directly in orbit, enabling autonomous perception, decision-making, and task execution. These systems are designed to support real-time intelligent services under the stringent constraints of space environments, including limited onboard computing resources, power budgets, and communication bandwidth. This field faces fundamental challenges arising from the mismatch between the computational demands of large-scale AI models and the constrained resources of satellite platforms. The high computational complexity of model inference, combined with limited onboard processing capabilities, makes it difficult to achieve low-latency responses. In addition, the harsh space environment introduces reliability concerns, requiring robust fault-tolerant mechanisms and adaptive system designs. Addressing these issues requires system-level co-design across model optimization, resource-aware scheduling, and fault-tolerant execution.
（2）Space-Based Computing Architecture
Space-based computing architecture focuses on designing scalable, efficient, and reliable computing infrastructures for large-scale satellite constellations. It aims to transform traditional satellites into collaborative computing nodes, forming a distributed space computing network capable of supporting complex applications. A key challenge lies in the dynamic and time-varying nature of space networks, where topology changes, intermittent connectivity, and heterogeneous resources complicate system design. The integration of computing, communication, and sensing further increases system complexity, requiring unified architectural frameworks and cross-layer optimization strategies. Addressing issues such as resource allocation, task scheduling, system scalability, and fault tolerance is essential for building a resilient and high-performance space computing ecosystem.
（3）Integrated Computing–Communication–Sensing Systems
Integrated computing–communication–sensing systems aim to enable unified and efficient space information processing across multiple domains. The core challenge lies in the strong coupling among computing, communication, and sensing, leading to complex trade-offs in latency, energy, and performance. In addition, dynamic environments introduce uncertainties that affect system stability. Solving these problems requires unified frameworks and adaptive mechanisms for efficient and robust system integration.
Group openings: We are currently recruiting outstanding talents (Master’s studentsand interns) with a strong passion for scientific research!
代表作：
[1]Liu Y, Han Y*, Li H, et al. "Computing over Space: Status, Challenges, and Opportunities." Engineering, 2025, 54(11): 20-25. DOI:10.1016/j.eng.2025.06.005.
[2]Li H, Liu Y*, Shi J, et al., "Multi-Attribute and Multi-Point Cooperative Handover Strategy for LEO Satellite Communication Systems", China Communications, 2026, vol. 23, no. 1, pp. 154-165. DOI: 10.23919/JCC.fa.2023-0168.20260.
[3] 史冰冰,李泓辛,刘垚圻,等.面向天基计算的故障表征与容错机制[J].天地一体化信息网络,2026,7(01):91-101.
[4]Liu Y*, Shi B, Han Y, et al. "Efficient Information Support in LEO SCNs: A Multi-Layer Spatiotemporal Cost Model and Dynamic Reallocation Algorithm." Computer Networks, 2025: 111792.
[5]李红光,石晶林,周一青,刘垚圻. 基于DWGA的低轨卫星多波束调度策略[J].西安电子科技大学学报, 2025, 52(3):73-84.
[6]Su H, Liu Y*, Zhou Y, et al. "A parallel discrete event simulation engine for the low-earth-orbit satellite constellation networks," China Communications, 2024，vol. 21, no. 8, pp. 264-275.
[7]Li H, Sui Y, Liu Y*, et al. "Dynamic LSTM-Based Channel Quality Indicators Prediction for LEO Satellite Communication." IEEE Wireless Communications Letters 13.12 (2024): 3285-3289.
[8]李红光,刘垚圻,周一青,等.基于MBSE的卫星互联网仿真平台架构建模[J].电信科学, 2024, 40(9):1-12. DOI:10.11959/j.issn.1000-0801.2024201.
[9]Su Y, Liu Y, Zhou Y, et al. Broadband LEO Satellite Communications: Architectures and Key Technologies. IEEE Wireless Communication, 2019(26): 55-61.（ESI高被引）
