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Feng Zhang

Associate professor
Master Tutor

Gender : Male
Alma Mater : Beihang University,Sun Yat-sen University
Education Level : Faculty of Higher Institutions
Degree : Doctoral Degree in Engineering
School/Department : School of Computer Science
Discipline : Computer science and technology
Contact Information : Department of Computer Science, China University of Geosciences, No. 68, Jincheng Street, Donghu New Technology Development Zone, Wuhan, Hubei, China
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Personal Profile

Feng Zhang is currently an associate professor in the School of Computer Science at the China University of Geosciences, Wuhan, China. He received his PhD and MSc degrees from the Sun Yat-sen University, and BSc degree from the Beihang University, all in computer science. He was a visiting scholar of Kent State University, USA, in 2012-2013. Dr. Zhang has a comprehensive academic background, having published over 50 peer-reviewed research papers, including some in top-tier journals and conferences, TDSC, TC, TII, JPDC, Information Sciences, AAAI, and more. His training encompasses both academic and engineering aspects.


He is/was the PI of several research projects and a core investigator of a key project of NSFC. He also played a key role in the development and deployment of several large-scale computer engineering projects. He serves as a reviewer of many prestigious international journals, such as TKDE, TIST, TII, TIFS, TDSC, KnoSys, and IET Information Security. He also serves as a thesis reviewer for the Ministry of Education, China.


Dr. Zhang has long been the advisor to the ACM-ICPC club, a student association aimed at enhancing the students' cutting-edge coding ability. His commitment to fostering students' "discipline competition excellence" and "academic excellence" has resulted in numerous awards at the provincial level and above in competitions such as ACM-ICPC, CPC/PAC supercomputing, CCPC, and Lanqiao programming. His students have achieved significant recognition, including the publication of SCI papers and success in national scholarship competitions. Many of his graduates have pursued further studies at prestigious universities and research institutes globally or secured positions in renowned IT companies like Alibaba, Tencent, Baidu, ByteDance, and others.


Dr. Zhang's research interests span machine learning, data science, high-performance computing, and secure AI. His primary focus revolves around the performance and security of mining and deductive knowledge in big data. He is dedicated to advancing theories, methods, technologies, and tools to address these concerns. 

If you are a dedicated student into scientific research or in-depth IT technology, are a good team player, and have the personalities of being optimistic, tough, stress-resistant, you are welcome to apply for the post-graduate vacancies of Dr. Zhang. A strong background in mathematics, algorithms, or English is a plus for your application. Email: jeff.f.zhang@gmail.com.

Selected publications(corresponding author marked with *)

[1] Wenbin Huang (Graduate Student), Rui Tang (Graduate Student), Guangzhi Qu, Feng Zhang*. An XGBoost-Based Method for Improved Orbit Prediction with an Orbit-Separate Modeling Strategy. IEEE Transactions on Aerospace and Electronic Systems. 2024, Accepted.

[2] Feng Zhang*, Zihao Wang (Graduate Student), Ruixin Guo (Graduate Student), Guangzhi Qu. Earth Observation Data Provenance: A Blockchain-based Solution. IEEE Transactions on Industrial Informatics. 2024, Accepted.

[3] Feng Zhang*, Hao Wang (Graduate Student), Erkang Xue (Graduate Student), Ruixin Guo (Graduate Student), and Guangzhi Qu. Efficient Differentially Private Tensor Factorization in the Parallel and Distributed Computing Paradigm. In: Proceedings of the 21st IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2023). 

[4] F. Zhang*, E. Xue (Graduate Student), R. Guo (Graduate Student), G. Qu, G. Zhao, and A. Zomaya. DS-ADMM++: A Novel Distributed Quantized ADMM to Speed up Differentially Private Matrix Factorization. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(6):1289 - 1302.  

[5] R. Guo (Graduate Student), F. Zhang*, L. Wang, W. Zhang, X. Lei (Graduate Student), R. Ranjan, and A. Zomaya. BaPa: A Novel Approach of Improving Load Balance in Parallel Matrix Factorization for Recommender Systems. IEEE Transactions on Computers, 2021, 70(5): 789-802. (Featured Paper of the Month). 

[6] X. Lei (Graduate Student), R. Guo (Graduate Student), F. Zhang*, L. Wang, R. Xu, and G. Qu.  Optimizing FHEW with Heterogeneous High-Performance Computing. IEEE Transactions on Industrial Informatics. 2020, 16(8): 5335-5344. 

[7] F. Zhang*, V. E. Lee, R. Jin, S. Garg, K.-K. R. Choo, M. Maasberg, L. Dong, and C. Cheng. Privacy-aware Smart City: A Case Study in Collaborative Filtering Recommender Systems. Journal of Parallel and Distributed Computing. 2019, 127: 145-159.

[8] F. Zhang*, V. E. Lee, and K.-K. R. Choo. Jo-DPMF: Differentially Private Matrix Factorization Learning Through Joint Optimization. Information Sciences. 2018, 467: 271-281. 


Multivariate Statistics and Matrix Analysis (Spring 2022, 2023, 2024)

Multivariate Statistical Analysis (Fall 2021, 2022, 2023)

Data Mining and Machine Learning (Graduate level, Fall 2020, 2021, 2022, 2023)

Information Content Security (Fall 2019, Fall 2020)

Information Security Theory and Technology (Graduate level, Spring 2019, 2020, 2021)

Secure Coding (2013-2019)

C/C++ Programming (2011-2021)

Communication Resource Management System (International students, Spring 2014, 2015)

Education Background

  • Sun Yat-sen University       Computer science and technology       Doctoral Degree       Doctoral Degree in Engineering

  • Sun Yat-sen University       Computer science and technology       Master's Degree       Master's Degree

  • Beihang University       Computer science and technology       Bachelor's Degree       Bachelor's Degree

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Research Focus

  • HPC (Cost-constraint Multi-core and Many-core Optimizations, Heterogeneous Supercomputing)
  • Machine Learning (Optimization Theories and Methods, Tensor Computation, Recommender Systems)
  • Mining and Analysis of Earth Observation and Aerospace Data based on the above Techniques