Address：

Rm.2-108, Tsinghua-Rohm Electronic Engineering Hall

Tsinghua University, Beijing 100084, China

Tel:+86-10-62783389

Fax:+86-10-62797073-808

E-mail:：yangzhp@mail.tsinghua.edu.cn

Education:

2020-2025 Ph.D.in Shanghaitech University

2016-2020 B.S.in Xidian University 学士

Working Experiences:

2025-Now: Postdoc, Dept, of Electronic Engineering, Tsinghua University

Professional Experience:：

Dr. Yang’s research focuses on key technologies in edge artificial intelligence (AI). During his doctoral studies, he developed high-performance, reliable, and low-latency methods for training and inference of AI models at the edge, integrating enabling technologies such as  blockchain, federated learning, and microservices. Within this architecture, he proposed AI-based resource optimization algorithms to intelligently coordinate sensing, computing, and communication resources. In his postdoctoral research, Dr. Yang shifted focus to the fundamental scientific and engineering challenges of real-time spectral imaging chips. He developed AI-driven training and inference frameworks tailored for optoelectronic detection systems in industrial settings, with the goal of significantly improving both real-time performance and detection accuracy.

Publications:

[1] Chen Lin, Zhanpeng Yang, Ting Wang, Xin Liu, Yuning Jiang, Yong Zhou and Yuanming Shi , “Large Language Models for Microservice Deployment in Space Computing Power Networks”, in Proc. Globecom Workshops (GC Wkshps), Taipei, Taiwan, Dec. 2025.

[2] Z. Yang, P. Zhang, J. Zhu, D. Wen, Y. Shi and W. Chen, “Hierarchical Feder ated Learning with Integrated Sensing-Communication-Computation over Space-Air Ground Integrated Networks,” in Proc. IEEE Int. Conf. Commun. (ICC), Montreal, Canada, Jun. 2025.

[3] Z. Yang, Z. Yu, X. Liu, D. Wen, Y. Zhou and Y. Shi, “Latency-Aware Microservice Deployment for Edge AI Enabled Video Analytics,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Dubai, United Arab Emirates, Apr. 2024.

[4] Z. Yang,Y. Shi, Y. Zhou, Z. Wang, K. Yang, “Trustworthy federated learning via blockchain,” IEEE Internet Things J.,, vol. 10, no. 1, pp. 92-109, Jan. 2023.

[5] Z. Yang, Y. Zhou, Y,Wu, Y. Shi, “Communication-efficient quantized SGD for learning polynomial neural network,” in Proc. IEEE Int. Perform., Comput., Commun. Conf. (IPCCC), Oct. 2021.

[6] Y. Shi, K. Yang, Z. Yang, and Y. Zhou, “Mobile edge artificial intelligence: Opportunities and challenges,” Elsevier, Aug. 2021.