Chong Yu

Chong Yu

Asst Professor

Rhodes Hall

887

CEAS - Computer Science - 0030

Professional Summary

Dr. Yu's research interests include artificial intelligence, federated learning, and cybersecurity, with a broad range of applications including data analytics, edge-based AI, and the Internet of Things (e-health, intelligent agriculture, and connected vehicle).

I am seeking self-motivated Ph.D. students who have interests in the following areas, including but not limited to artificial intelligence (AI), federated learning, and cybersecurity. If you are interested, please email me a copy of your resume, GPA, GRE score (if applicable), and TOEFL/IELTS score (for international applicants). In your email, please use “Graduate Applicant – Your Name” as the subject, and describe the motivation of your interests. Multiple RAs will be provided to outstanding applicants, starting from Spring 2024 or Fall 2024

Education

Bachelor of Science in Communication Engineering: Northeastern University, Shenyang, China, 2015

Master of Science in Communication and Information System: Northeastern University, Shenyang, China, 2017

Ph.D. in Electrical and Computer Engineering: University of Nebraska-Lincoln, Lincoln, USA, 2023

Publications

Peer Reviewed Publications

C. Yu, S. Shen, H. Yang, K. Zhang and H. Zhao (2022. ) Leveraging Energy, Latency and Robustness for Routing Path Selection in Internet of Battlefield Things . IEEE Internet of Things Journal, , 9 (14 ) ,12601 -12613

C. Yu, S. Shen, K. Zhang, H. Zhao, and Y. Shi (2022. ) Energy-Aware Device Scheduling for Joint Federated Learning in Edge-assisted Internet of Agriculture Things . in Proc. of IEEE WCNC'22, , 1140 -1145

C. Yu, S. Shen, S. Wang, K. Zhang, and H. Zhao (2022. ) Efficient Multi-Layer Stochastic Gradient Descent Algorithm for Federated Learning in E-health . in Proc. of IEEE ICC'22, , 1263 -1268

S. Shen, C. Yu, K. Zhang, and S. Ci (2022. ) Collaborative Edge Caching with Personalized Modeling of Content Popularity over Indoor Mobile Social Networks . in Proc. of IEEE ICC'22, , 4114 -4119

S. Shen, C. Yu, K. Zhang, J. Ni, and S. Ci (2021. ) Adaptive Artificial Intelligence for Resource-Constrained Connected Vehicles in Cybertwin-Driven 6G Network . IEEE Internet of Things Journal, , 8 (22 ) ,16269 -16278

S. Shen, C. Yu, K. Zhang, J. Ni, and S. Ci (2021. ) Adaptive and Dynamic Security in AI-Empowered 6G: From an Energy Efficiency Perspective . IEEE Communications Standards Magazine, , 5 (3 ) ,80 -88

S. Shen, C. Yu, K. Zhang, X. Chen, H. Chen, and S. Ci (2021. ) Communication-Efficient Federated Learning for Connected Vehicles with Constrained Resources . in Proc. of IEEE IWCMC'21, , 1636 -1641

W. Yao, K. Zhang, C. Yu, and H. Zhao (2021. ) Exploiting Ensemble Learning for Edge-assisted Anomaly Detection Scheme in e-healthcare System . in Proc. of IEEE Globecom'21, , 1 -7

S. Shen, C. Yu, K. Zhang, and S. Ci (2021. ) Exploiting Feature Interactions for Malicious Website Detection with Overhead-accuracy Tradeoff . in Proc. of IEEE ICC'21, , 1 -7

C. Yu, S. Si, H. Guo, and H. Zhao (2018. ) Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication . Sensors, , 18 (9 ) ,2971 -2986

Courses Taught

Data Structures Level:Undergraduate

Contact Information

Rhodes Hall 887
yuc5@ucmail.uc.edu