To put a dent in the universe.
I am a fourth-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.
Zhou, Z.*, Yeung, W.*, Soleymani, S.,Gravel, N., Salcedo, M., S., Li, S& Kannan, N. Explaining the blackbox - Unraveling Protein Language Model's Learning Mechanisms for Kinase-Specific Phosphorylation Prediction. ICML Workshop on Computational Biology (Spotlight)
Zhang, J., Zhang, D., Chen, Z., Zhou, Z., Mai, G., Mu, L. Identifying and Intervening in Key Predictors of Out-of-Hospital Cardiac Arrest Survival Outcome Using Explainable Artificial Intelligence. ICML 3rd Workshop on Interpretable Machine Learning in Healthcare
Zhang, J.*, Zhou, Z.*, Mai, G., Hu, M., Lan, M., and Li, S., Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models. (preprint)
Guan, Z.*, Hu M.*, Zhou, Z.*, Zhang, J., Li, S., Liu, N., BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks. (preprint)
Zhou, Z.*, Hitt, N., Letcher, B., Shi, W., and Li, S., 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, M, S., Soleymani, S, Li, S& Kannan, N. Phosformer: An explainable Transformer model for protein kinase-specific phosphorylation prediction. Bioinformatics, 2023.
Yeung, W.*, Zhou, Z.*, Li, S. & Kannan, N. Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings. Briefing in Bioinformatics, 2023(*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. Briefing in Bioinformatics, 2023(*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.
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
Briefing in Bioinformatics Reviewer
AAAI 2023 AI for Web Advertising Workshop PC Member
Teaching assistant for CSCE 145 - Algorithmic Design I
Guest Lecturer for CSCI 3360 Data Science