To put a dent in the universe.

——Steve Jobs

Zhongliang Zhou(周中良)

  • I am a third-year Ph.D. student majoring in Computer Science at the University of Georgia co-advised by Dr.Natarajan Kannan and Dr.Sheng Li. Before joining UGA, I obtained my Bachelor's degree in Computer Science and Technology from Harbin Institute of Technology advised by Dr.Yongdong Xu.

  • My main research interest focus on interdisciplinary research, Machine Learning, and Deep learning with application in bioinformatics, and sequential data. I am also interested in explainable machine learning(XAI).

  • In my spare time, I am keen on photography, tennis, and badminton.

  • I am fortunate to have the cutest puppy in the world named Juan Mao(卷毛), his picture is in the photo gallery.


  • I received 2022 Outstanding Graduate Student award from University of Georgia Department of Computer Science!


  • Zhongliang Zhou, Nathaniel Hitt, Benjamin Letcher, Weili Shi, and Sheng Li. Pigmentation-based Visual Learning for Salvelinus fontinalis Individual Re-identification. IEEE International Conference on Big Data (IEEE BigData), 2022.

  • Zhou, Z.*, Yeung, W.*, Gravel, N., Salcedo, S., Soleymani, S, Li, S& Kannan, N. (2022)An explainable deep learning model for protein kinase-specific phosphorylation prediction (Co-first author submitted)

  • Yeung, W.*, Zhou, Z.*, Li, S. & Kannan, N. (2022). Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings(*co-first author)

  • Yeung, W.*, Zhou, Z.*, Li, S. & Kannan, N. (2022). Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies(*co-first author)

  • Yeung, W.*, Zhou, Z.*, Mathew, L. G., Gravel, N., Taujale, R., Venkat, A., ... & Kannan, N. (2022). An explainable unsupervised framework for alignment-free protein classification using sequence embeddings. bioRxiv.(*co-first author)

  • Berger, B. M., Yeung, W., Goyal, A., Zhou, Z., Hildebrandt, E. R., Kannan, N., & Schmidt, W. K. (2022). Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters. PloS one, 17(6), e0270128.

  • Taujale, R.*, Zhou, Z.*, Yeung, W., Moremen, K. W., Li, S., & Kannan, N. (2021). Mapping the glycosyltransferase fold landscape using interpretable deep learning. Nature communications, 12(1), 1-12.(*co-first author)

  • Taujale, R., Venkat, A., Huang, L. C., Zhou, Z., Yeung, W., Rasheed, K. M., ... & Kannan, N. (2020). Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases. Elife, 9, e54532.

  • Li, M., Zhang, J., Wu, B., Zhou, Z., & Xu, Y. (2018). Identifying Keystone Species in the Microbial Community Based on Cross-Sectional Data. Current gene therapy, 18(5), 296-306.

Work Experience

  • Machine Learning Intern Norfolk Southern May 2022-August 2022

  • Data Scientist Intern BASF August 2021-March 2022

  • Front-end SDE Intern HIT August 2018-June 2019

Teaching Experience

  • Teaching assistant for CSCE 145 - Algorithmic Design I

  • Guest Lecturer for CSCI 3360 Data Science

Photo Gallery