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.
News
I received 2022 Outstanding Graduate Student award from University of Georgia Department of Computer Science!
Publication
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