Yiqian Zhang

Candidate for B.S. in Statistics and Double Major in Mathematics (Applied Mathematics Concentration)

Sep 2021 - May 2025

University of Illinois Urbana-Champaign

• GPA: 3.88/4.00
• Selected Coursework: Abstract Linear Algebra, Ordinary/Partial Differential Equations, Statistical Modeling, Statistics Programming Methods, Fundamental Mathematics, Real Analysis, Design of Experiments, Applied Complex Variables, Statistical Learning, Applied Bayesian Analysis, Statistical Learning, Mathematical Statistics.
• Honors & Awards: Dean’s list in Spring 2022, Spring 2023 and Spring 2024, Hoover Mathematical Scholar Award.

Zhang, Y., Schluter, J., Zhang, L., Cao, X., Jeng, R. R., Feng, H., Haines, J., & Zhang, L., “Review and Revamp of Compositional Data Transformation: A New Framework Combining Conversion Normalization and Contrast Transformation”, Computational and Structural Biotechnology Journal (CSBJ), vol. 23, pp. 4088-4107, 2024. DOI: 10.1016/j.csbj.2024.11.003

Course Assistant — Statistical Modeling I (STAT 425)

Aug 2024 - Present

University of Illinois at Urbana-Champaign, Statistics Department

• Graded assignments on Gradescope, ensuring accurate evaluation and timely feedback for students.
• Proctored in-person exams, maintaining academic integrity during assessments.

Course Assistant — Statistics Programming Methods

Aug 2024 - Present

University of Illinois at Urbana-Champaign, Statistics Department

• Assisted in virtual exam proctoring and final exam grading for two major exams, ensuring academic integrity and timely assessment.

Course Assistant — College Algebra

Aug 2023 - Present

University of Illinois at Urbana-Champaign, Mathematics Department

• Conducted group discussions and office hours for 56 students, covering topics like factoring, equations, functions, and polynomials.
• Assisted in grading, problem-solving, and course optimization.

Tutor — Calculus & Calculus 1

Aug 2023 - Dec 2023

University of Illinois at Urbana-Champaign, Mathematics Department

• Provided tutoring in Calculus and Analytic Geometry to over 50 students weekly.
• Enhanced students’ understanding and performance in assignments, quizzes, and exams

UIUC Student Distribution Analysis - Link

Mar 2023 - May 2023

• Developed an R Shiny application to visualize UIUC student distribution, enabling dynamic data exploration.
• Integrated various data visualizations like bar and pie charts, and a map feature to illustrate student distribution across the U.S.
• Provided detailed data tables for a comprehensive state-and-year-specific student breakdown.

Advanced SQL: MySQL Data Analysis & Business Intelligence Project

Mar 2022 - Jul 2022

• Analyzed e-commerce website traffic using advanced SQL techniques, identifying key traffic sources and user behavior patterns
• Developed and implemented marketing strategies that improved conversion rates by 15% and reduced bounce rates by 10%.
• Performed statistical tests to optimize budget allocation, resulting in a 20% efficiency increase.

Covid-19 Data Visualization by Tableau

Oct 2021 - May 2022

• Utilized Tableau to create dynamic dashboards visualizing global COVID-19 trends, including case and death counts.
• Analyzed daily new cases to identify patterns and regions of concern.
• Designed intuitive and informative visuals for enhanced data interpretation and user experience.

Language

English (Advanced), Mandarin Chinese (Native)

Technical Skills

Python, SQL, R, Linux, DESeq2, Kraken2, QIIME 2, Git, LaTeX, JAVA, Tableau, MongoDB, C++, Docker, Microsoft Office

Professional Competence

Machine Learning, Data Mining, Data Visualization, Linear Algebra and Calculus, Probability and Statistics, Data Management, Web Scraping and APIs, Geographic Information Systems, Database Management

Comprehensive Review and Methodological Advancement in Beta Diversity Measures

Sep 2024 - Present

Assistant Under the Guidance of Dr. Zihan Zhu (Case Western Reserve University)

• Project Focus: Conducted a comprehensive review of dissimilarity measures and their mathematical properties (e.g., conditional negative definiteness) and evaluated their empirical performance across diverse real-world datasets.
• Methodology: 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.
• Innovation: First systematically reviewed the mathematical properties of dissimilarity measures and evaluated their consequence in downstream analysis. Meanwhile, proposed remedial strategies for dissimilarity measures without ideal mathematical properties.
• Outcome and Dissemination: Currently preparing a manuscript for submission to a peer-reviewed journal, anticipated in January 2025. This paper aims to provide practitioners with methodological insights and introduce enhanced tools for beta diversity analysis.

Microbiome Analysis for Alzheimer's Disease Pilot Study

May 2024 - Present

Research Assistant Under the Guidance of Professor Lijun Zhang and Professor Liangliang Zhang(CWRU)

• Project Focus: Investigated the role of the microbiome in Alzheimer's Disease-like phenotypes as part of the Jax.IU.Pitt Microbiome Pilot Study, aiming to identify microbiome-related factors contributing to Late Onset Alzheimer's Disease (LOAD).
• Methodology: 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.
• Analysis and Visualization: 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.
• Tutorial Creation: Created a comprehensive tutorial on using QIIME 2 for microbiome analysis. Website: https://yiqianzhang.com/hpc_tutorial/

Review and Revamp of Microbiome Compositional Data Transformation

Nov 2023 - Dec 2024

Research Assistant Under the Guidance of Professor Liangliang Zhang(CWRU)

• Conducted a systematic review on compositional data transformations, focusing on the strengths and limitations of existing techniques, such as rarefaction, scaling, and transformation, in microbiome data analysis.
• Developed a novel framework combining proportion conversion with contrast transformations (CCT Framework) to enhance data handling in high zero-inflation microbiome datasets, opening new avenues for advancements in compositional data transformation across diverse applications.
• Proposed two new transformation methods, Centered Arcsine Contrast (CAC) and Additive Arcsine Contrast (AAC), demonstrating improved performance in stabilizing variance and mitigating outlier influence, particularly for high zero-inflation microbiome data. These methods support applications such as differential abundance testing, variable selection, and distance-based analyses.
• Proposed a dual-group conversion framework for proportional data, including the Dual-Group Truncated Logit (DGTL) conversion, specifically beneficial for two-sample testing. This framework allows transformed data to follow two independent distributions, achieving an effective balance between preserving pre-conversion information and enhancing the power to detect differential abundance.

Computational Modeling of MDD and Bipolar Disorder

Jan 2024 - Present

Research Assistant Under the Guidance of Dr.Brandon Brown(Carle Foundation Hospital) and Professor Xinzhu Yu

• Create computational models that mimic mood fluctuations, with a specific emphasis on simulating Major Depressive Disorder (MDD) and Bipolar Disorder (BD).
• Utilize Markov chain and Hidden Markov Models to portray mood states, encompassing normal, depressive, and manic phases, along with transitions influenced by a combination of internal and external factors.
• Conduct in-depth statistical examination to assess the prevalence and trends of both MDD and BD, mirroring actual epidemiological data.

Network Dynamics Analysis through Preferential Attachment Models

Jan 2024 - Present

Research Assistant Under the Guidance of Professor Yuexi Wang and Professor Yuguo Chen

• Implemented simulations in Python, leveraging libraries such as NetworkX for network generation and Matplotlib for visualization.
• Applied Bayesian inference and Markov Chain Monte Carlo (MCMC) methods to estimate model parameters, aligning simulations with observed network characteristics.
• Analyzed network formation processes by comparing simulated degree distributions with empirical data, utilizing statistical methods to validate model accuracy.

Rail Transportation and Engineering Center (RailTEC), Rail Safety and Risk Group

Oct 2023 - Present

Undergraduate Research Assistant Under the Guidance of Professor Christopher P. L. Barkan

• Engaged in comprehensive railway transportation safety projects, applying engineering, analytical, and operational principles to real-world challenges.
• Developed and implemented a Python script to automate the weekly Tank Car Report, significantly enhancing team efficiency by 96%.
• Authored and contributed to detailed reports, presenting findings and recommendations to enhance railway safety protocols and procedures.
• Utilized Python for data management and analysis. Successfully processed the ’CarList2022’ dataset, a comprehensive 25GB+ collection of vehicle information. Integrated this with the ’FRA’ dataset, which details car accidents. This involved meticulous data matching and merging, with a focus on correlating car accidents with vehicle length, ensuring data consistency and accuracy for in-depth research analysis.
• Collaborated with a multidisciplinary team of engineers and researchers, continuously expanding knowledge in transportation risk analysis and railway engineering.

Ukraine Data Analysis - Link

May 2023 - Jul 2023

Undergraduate Research Assistant Under the Guidance of Professor Richard B. Sowers

• Created and executed a comprehensive data analysis pipeline using Jupyter Notebooks to analyze and visualize the Armed Conflict Location and Event Data Project (ACLED) in Ukraine using Python, pandas, folium, and MongoDB.
• Constructed choropleth maps to compare differences in military engagements across various cities and time frames, utilizing Open StreetMap API for data extraction.
• Employed advanced data visualization techniques such as heatmaps, word frequency distributions, and time series animations to provide dynamic insights into the conflict data.
• Stored and managed over 10 GB of data in MongoDB, achieving fast and efficient queries and updates.
• Documented all processes and methodologies in detailed notebooks and an API guide, enhancing the accessibility and usability of the data analysis pipeline for future researchers and stakeholders.