Haihui Yan

• Manage the end-to-end clinical research data lifecycle for therapeutic brain intervention projects, focusing on database design and data integrity within the Temerty Centre. • Develop and maintain complex REDCap databases for clinical trials, including the implementation of automated data quality rules and longitudinal instrument design. • Leverage Python (pandas) to automate clinical data cleaning and processing, specifically streamlining the analysis of concomitant medications and treatment timelines. • Collaborate with principal investigators and clinical coordinators to provide technical support, ensuring all data management practices align with project design checklists and validation standards.

Project: Customer Retention E-commerce Analytics • Developed an interactive Looker Studio dashboard visualizing cohort retention trend, KPIs, and repurchase behaviors across time, devices, and channel; Leveraged SQL to preprocess session- and transaction-level data. • Performed cohort-based retention analysis, uncovering that only 4–6% of users remained active after 3–6 months, with most repeat purchases occurring beyond the 60-day mark. • Applied observational A/B testing logic to assess marketing channel performance, finding higher repeat purchase rates among Paid Search and Referral cohorts; identified stronger long-term retention among tablet users compared to mobile users, informing device-specific marketing and UX optimization strategies. Project: Healthcare Analytics • Led a small team of contractors, assigning analytical tasks and reviewing outputs to ensure methodological consistency and timely delivery. • Explored population-level mental health indicators across Ontario (2009–2022) in R using datasets from Public Health Ontario, covering 6.9MB dataset, focusing on emergency department (ED) visit rates. • Ensured data integrity through quality control checks on healthcare datasets involving missing data checks, ensuring data consistency, validating data. • Developed generalized linear models (GLMs) and mixed-effects models to assess the impact of ON-Marg social determinants—income, housing, labor force engagement, and racialized status—on mental health outcomes.

• Performed statistical analysis for a double-blind, placebo-controlled randomized trial assessing the impact of zinc and dietary Sadenosylmethionine (SAMe) supplementation on lung health for 100 participants with alcohol use disorder. • Applied mixed-effects models to evaluate treatment effects over time; conducted Kruskal-Wallis and Dunn’s post-hoc tests to compare serum zinc level changes. Identified significant improvements in serum zinc levels for specific treatment groups, contributing to insights on nutritional interventions for immune health and personalized health strategies for individuals with alcohol use disorder.

• Explored the impact of site characteristics and prior performance on clinical trial risk assessment; leveraged site-level key risk indicators (KRIs) such as screen failure rates, enrollment trends, and protocol deviations, alongside historical performance data from multiple clinical studies. • Conducted exploratory data analysis in R using density and distribution plots to assess site-level risks. • Prepared clinical progress reports summarizing enrollment status, data quality findings, and key insights for the clinical operations and data management teams. • Enhanced trial monitoring by implementing dynamic Power BI visualizations, facilitating early identification on high-risk sites.

• Served as a Teaching Assistant for a graduate-level biostatistics course focused on SAS programming, data manipulation, and clinical data management. • Assisted students in learning key SAS procedures including PROC SQL, PROC REG, PROC LOGISTIC, and PROC MEANS, reinforcing applications in epidemiological and biomedical research. • Supported grading and debugging SAS code to ensure accurate data outputs and adherence to analytical standards.

• Served as a Teaching Assistant for the Department of Quantitative Theory and Methods, supporting an undergraduate statistics course with over 100 students. • Assisted students with R programming, data cleaning, and visualization using packages such as tidyverse, ggplot2, and dplyr, reinforcing applied concepts in probability, regression, and hypothesis testing. • Held weekly office hours to provide one-on-one guidance, clarifying statistical concepts and coding logic. • Contributed to the development of reproducible teaching materials and R Markdown tutorials to help students document their analytical workflow effectively.

• Collected and analyzed Caenorhabditis elegans locomotion data using a tracking microscope and DeepLabCut, processing 20+ video datasets with labeled neural activity. • Developed Python scripts for data processing, including filtering raw data, calculating head angles, and automating time-series analysis; generated key visualizations such as movement trajectory plots, frequency analysis, and autocorrelation graphs. • Provided graduate researchers with a quantitative framework for neural behavior analysis, enhancing motion tracking efficiency and supporting future studies on motor control and neural circuit mechanisms.

• Analyzed the data of customer feedback and provided reports about the ways to improve efficiency for the medical information. • Skillfully provided statistical analysis of data and delivered reports with timeliness and accuracy. • Communicated closely with external partners to process data requests within desired time frames.

Linear Algebra, Probability, Computational Biology Method, Python Programming

Biostatistics Methods | Epidemiologic Methods | Clinical Trials | Machine Learning | Applied Linear Models | Survival Analysis Methods | SAS Programming