About me

I am currently a Ph.D. student in the Department of Electrical and Computer Engineering at Wayne State University, under the supervision of Prof. Xingyu Zhou. Prior to my doctoral studies, I received my B.E. in Software Engineering from Beijing Jiaotong University.

I have gained valuable industry experience as a co-founding member at Neurospark (Nanjing) Technology Co., Ltd. Additionally, I worked as a Deep Learning Intern at China Railway Rolling Stock Corporation (CRRC), focusing on abnormal behavior detection systems.

Research Interests

My research primarily centers on the intersection of machine learning and stochastic systems, with a specialized focus on the following key areas:

Education

Ph.D. in Computer Science, Wayne State University, 2025 - 2030 (Expected)

B.E. in Software Engineering, Beijing Jiaotong University, 2020 - 2024

Publications

Research on Serverless Edge Computing Container Caching Strategies for Cost Optimization

Yi He, et al.

Graduation Project, 2024

Full Paper | Code Repository

Online Energy-Efficient and Elastic Cloud Resource Provisioning with QoS Guarantee

Ran Yan, Shuaibing Lu, Jie Wu, Yi He, Xinyu Deng, Shen Wu

Submitted for Publication, 2024

Preprint | Code Repository

Nmr-VSM: Non-Touch Motion-Robust Vital Sign Monitoring via UWB Radar Based on Deep Learning

Zhonghang Yuan, Shuaibing Lu,Yi He, Xuetao Liu and Juan Fang

Micromachines, JCR Q2, 2023

Full Paper | Code Repository

Projects

GlucoInsight: Advanced Blood Glucose Monitoring Assistant

Developed an innovative system utilizing cutting-edge algorithms from UCL laboratories for comprehensive diabetes management. The system features real-time glucose monitoring and personalized health recommendations, powered by exclusively licensed technology. This groundbreaking project received multiple awards at AdventureX 2024.

Project Details | GitHub Repository

Cloud-based Physical Fitness Test Platform

Engineered a platform for automated physical fitness assessment, integrating advanced motion tracking technology for real-time exercise form analysis. This innovative system has significantly improved the efficiency and accuracy of fitness evaluations, and has been adopted by dozens of primary, secondary, and high schools across China. Its widespread implementation demonstrates its effectiveness in enhancing physical education programs nationwide.

Project Details | GitHub Repository

Awards