Biostatistics & Microbiome Methods
Ph.D. Student in Biostatistics
Aspiring Biostatistician with a strong foundation in Mathematics and Statistics. Specializing in microbiome data analysis, compositional data transformation, and network dynamics. Experienced in developing novel statistical frameworks and computational models.
I am honored to receive the Student Poster Presentation Award at the 2026 Annual Joint Biostatistics Symposium for my research. It is a great privilege to have my work recognized by the community!
I will be presenting my latest research at the ENAR 2026 Spring Meeting. Please stop by during the Sunday evening Opening Mixer and Poster Session on March 15. I welcome the opportunity to discuss my work and potential collaborations! Feel free to get in touch beforehand.
Ph.D. in Biostatistics
B.S. in Statistics and Double Major in Mathematics (Applied Mathematics Concentration)
GPA: 3.91/4.00
Z. Zhu, Y. Zhang, W. Li, M. Greenacre, S. Saha, Y. Shi, and L. Zhang. "Mathematical Foundations of Beta Diversity: Why Common Metrics Fail in Microbiome Analysis." Journal of Statistical Theory and Applications, vol. 25, issue 1, pp. 9.
Z. Zhu, Y. Zhang, S. Lin, and L. Zhang. "DeSHAPE: Decomposing Ecological Structure through Heterogeneity, Asymmetry and Pattern Evaluation of α-Diversity." Bioinformatics.
Z. Zhu, Y. Zhang, R. Liu, W. Li, Z. Huang, X. Chen, Y. Duan, and L. Zhang*. "Koinetic: A Graph-based Distance as a New Ecological Metric for Microbial Community Differential Analysis." Briefings in Bioinformatics.
Y. Zhang, J. Schluter, L. Zhang, X. Cao, R. R. Jenq, H. Feng, J. Haines, and L. Zhang. "Review and Revamp of Compositional Data Transformation: A New Framework Combining Proportion Conversion and Contrast Transformation." Computational and Structural Biotechnology Journal, vol. 23, pp. 4088-4107.
Case Western Reserve University
Advisor(s): Dr. Zihan Zhu
Conducted a comprehensive review of dissimilarity measures and their mathematical properties, such as conditional negative definiteness, and evaluated their empirical performance across diverse real-world datasets.
Systematically evaluated existing dissimilarity measures to determine which ones truly satisfy conditional negative definiteness. Employed PCoA, PERMANOVA, miRKat and rigorous simulation frameworks for thorough method validation.
First systematically reviewed the mathematical properties of dissimilarity measures and evaluated their consequence in downstream analysis. Proposed remedial strategies for dissimilarity measures without ideal mathematical properties.
Currently preparing a manuscript for submission to a peer-reviewed journal aimed at providing practitioners with methodological insights and enhanced tools for beta diversity analysis.
Case Western Reserve University
Advisor(s): Prof. Liangliang Zhang and Prof. Lijun Zhang
Investigated the role of the microbiome in Alzheimer's Disease-like phenotypes as part of the Jax.IU.Pitt Microbiome Pilot Study to identify microbiome-related factors contributing to Late Onset Alzheimer's Disease.
Utilized QIIME 2 for microbiome data analysis, including quality control, taxonomic classification, and diversity analysis. Implemented advanced data transformation techniques to improve differential testing accuracy.
Conducted alpha and beta diversity analyses, creating interactive visualizations to present microbial community structures. Used statistical methods to evaluate changes in diversity metrics and the impact of metadata features.
Case Western Reserve University
Advisor(s): Prof. Liangliang Zhang
Conducted a systematic review on compositional data transformations focusing on strengths and limitations of existing techniques in microbiome data analysis.
Developed a novel framework combining proportion conversion with contrast transformations, the CCT Framework, to enhance data handling in high zero-inflation microbiome datasets.
Proposed two new transformation methods, Centered Arcsine Contrast and Additive Arcsine Contrast, demonstrating improved performance in stabilizing variance and mitigating outlier influence.
Proposed a dual-group conversion framework for proportional data, specifically beneficial for two-sample testing, enhancing the power to detect differential abundance.
Carle Health / UIUC
Advisor(s): Dr. Brandon Brown and Prof. Xinzhu Yu
Created computational models that mimic mood fluctuations, specifically simulating Major Depressive Disorder (MDD) and Bipolar Disorder (BD).
Utilized Markov chain and Hidden Markov Models to portray mood states (normal, depressive, manic) and transitions influenced by internal/external factors.
Conducted in-depth statistical examination to assess prevalence and trends, mirroring actual epidemiological data.
UIUC
Advisor(s): Prof. Yuguo Chen and Prof. Yuexi Wang
Developed and simulated Preferential Attachment models to study network evolution, focusing on the "rich-get-richer" dynamic.
Implemented simulations in Python using NetworkX for network generation and Matplotlib for visualization.
Applied Bayesian inference and Markov Chain Monte Carlo methods to estimate model parameters.
UIUC
Advisor(s): Prof. Christopher P. L. Barkan
Developed and implemented a Python script to automate the weekly Tank Car Report, enhancing team efficiency by 96%.
Utilized Python for data management and analysis of the 25GB+ "CarList2022" dataset integrated with the "FRA" dataset.
UIUC
Advisor(s): Prof. Richard B. Sowers
Created a data analysis pipeline in Jupyter Notebooks (Python, pandas, folium, MongoDB) to process and visualize Armed Conflict Location and Event Data.
Constructed choropleth maps, heatmaps, and time series animations to identify patterns and spatial-temporal dynamics.
Heliyon (Cell Press)
Statistics & Biostatistics Graduate Student Association (BSGSA), OSU
