Feng Zhang is currently an associate professor in the School of Computer Science at the China University of Geosciences, Wuhan. He received his PhD and MSc degrees from the Sun Yat-sen University, BSc degree from the Beihang University, all in computer science. He was a visiting scholar of Kent State University, USA, from 2012-2013. He has published more than 40 peer-reviewed research papers, including some in top-tier journals and conferences, TDSC, TC, TII, JPDC, Information Sciences, AAAI, and so on. He got proper training in both academics and engineering. 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, KBS, and IET Information Security. He was a visiting scholar of Kent State University, USA. He also serves as an MSc thesis reviewer of the Ministry of Education, China. 

Dr. Zhang loves his job as a university teacher. He has been serving as the advisor of the ACM-ICPC club (ACM-ICPC Association), a student association for cultivating the students' cutting-edge coding ability, for a long time. He devotes himself to developing and growing students' abilities of "discipline competition excellence" and "academic excellence". His students participated in discipline competitions such as ACM-ICPC, CPC / PAC supercomputing, CCPC, Lanqiao programming, winning nearly 200 awards at the provincial level or above. His students have published several SCI papers, among which there is a CCF A journal paper the graduate student as the first author, which was the first case in the college. He is the advisor of several graduates who won excellent thesis awards. He is also the advisor of several graduate students who were the finalists of the national scholarship. His students were picked to several national programs for enhancing their ability to innovate. His students entered prestigious universities and scientific research institutes at home and abroad for further study, or accepted offers from Alibaba, Tencent, Baidu, ByteDance, and other well-known IT companies.

Dr. Zhang has wide research interests, including HPC and machine learning. He is most concerned with the performance and security of the mining and deductive knowledge in big data, and devotes to studying theories, methods, technologies, and tools to address the 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: 8125345@qq.com.

Selected publications（corresponding author marked with *）

[1] 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.  (CCF A, T2)

[2] 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). (CCF A, T2)

[3] 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. (T1)

[4] 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 127: 145-159 (2019) (CCF B, T2)

[5] 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. (CCF B, T1)