Ning Zhang, Ph.D.

Assistant Professor of Computer Science and Data Science
Department of Mathematics and Computer Science
nzhang@fisk.edu

Education

  • Ph.D. in Computational Science, Middle Tennessee State University, 2019
  • M.S. in Computer Science, Middle Tennessee State University, 2017
  • M.S. in Signal and Information Processing, North China University of Technology, 2011
  • B.S. in Electronic Engineering, Qingdao University, 2008

Research Interests

Artificial Intelligence; Machine Learning; Deep Learning; Neuroimaging Data Analysis; Supervised Dimension Reduction

Brief Biosketch

Dr. Ning Zhang is an Assistant Professor of Computer Science and Data Science in the Department of Mathematics and Computer Science at Fisk University. His research focuses on artificial intelligence, machine learning, and deep learning, with particular interests in neuroimaging data analysis and supervised dimension reduction. His research applies advanced computational and data-driven methods to complex scientific problems, including functional MRI analysis.

Dr. Zhang’s research also extends to interdisciplinary applications of artificial intelligence and data science, including biomedical and chemical data analysis. He has participated in federally supported research and education projects funded by NSF, DOE, and USDA and is actively involved in developing research and training opportunities for undergraduate and graduate students.

Selected Publications

  1. Dong, W., Yuan, J., Yang, X., Zhang, N., & Zhang, M. (2025). Quantitative analysis of cannabinoids by zone heat-assisted DART-MS with in-situ flash derivatization. Forensic Chemistry, 100641.
  2. Alrammah, H., Gu, Y., Yun, D., & Zhang, N. (2024). Tri-objective optimization for large-scale workflow scheduling and execution in clouds. Journal of Network and Systems Management, 4, 89.
  3. Dong, W., Yang, X., Zhang, N., Chen, P., Sun, J., Harnly, J. M., & Zhang, M. (2024). Study of UV–Vis molar absorptivity variation and quantitation of anthocyanins using molar relative response factor. Food Chemistry, 444, 138653.
  4. Yang, X., Zhang, N., & Schrader, P. T. (2022). A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. Machine Learning with Applications, 8, 100290.
  5. Zhang, N., & Wu, Q. (2019). Online learning for supervised dimension reduction. Mathematical Foundations of Computing, 2(2), 95–106.
  6. Zhang, N., Yu, Z., & Wu, Q. (2019). Overlapping sliced inverse regression for dimension reduction. Analysis and Applications, 17(5), 715–736.