Ngoc Mai Le

• Implemented diverse statistical models (e.g., linear regression, logistic regression, mixed model effects) using STATA, enhancing the interpretability and clinical relevance of stroke data analysis, contributing to several publications on Al-driven diagnostics and treatment optimizations.
• Led statistical analysis for clinical research projects in collaboration with neurologists and biostatisticians, determining optimal methodologies that improved the accuracy of stroke data interpretation and informed clinical practices.
• Utilized Stata to clean and standardize multicenter registry data, resolving inconsistencies and reducing missing data, ensuring uniformity across datasets from multiple hospitals, which supported impactful studies on healthcare access and treatment disparities.
• Coordinated with research teams, including undergraduate students, medical students, residents, and research coordinators, to consolidate findings into comprehensive reports, leading to improved clinical workflows and published research.
• Gathered and analyzed patient health records from EMR systems to support ongoing research initiatives, enhancing the accuracy and quality of multicenter registry analysis using Excel and

• Developed statistical analysis plans and performed two-way ANOVA, one-way ANOVA, t-tests, non-parametric tests, and linear regression to support ongoing research initiatives and enhance data-driven decision-making; created comprehensive, user-friendly reports and assisted lab members with their analyses.
• Supervised and trained a new research assistant, enabling them to independently manage lab tasks and support other research scientists and postdoctoral fellows.
• Analyzed experimental data to identify patterns and implications relevant to cognitive decline in the Alzheimer's murine model, contributing to the advancement of research in neurodegenerative diseases.
• Designed protocols for behavioral studies that improved research accuracy and consistency, enhancing the reliability of experimental outcomes within the team.
• Collaborated with team members to develop new experiments aimed at understanding brain function and behavior, fostering innovation and advancing the lab's research capabilities.
• Conducted literature reviews to support research projects and ensure comprehensive understanding of current studies.

• Conducted statistical analysis to interpret the results of mouse behavioral tests, contributing to the overall research objectives.
• Executed advanced laboratory procedures, including histological analysis and immunohistochemistry, to support ongoing research projects.
• Managed mouse colonies and maintained records, ensuring compliance with laboratory standards and ethical considerations.
• Conducted literature reviews to support manuscript development, synthesizing relevant studies and data to strengthen research arguments.
• Participated actively in regular meetings with fellow researchers to discuss project updates, challenges faced, and lessons learned during ongoing activities.

• Company specializing in biomedical products and reagents.
• Designed Excel sheets for organizing data on potential buyers to streamline the sales process and improve accessibility for the sales team.
• Conducted literature reviews to identify trends in antibody research and support the company's product offerings.
• Mentored 10+ interns on research and data management, fostering their skills and enhancing their understanding of industry practices.

• Relevant Coursework: Categorical Data Analysis, Applied Linear Regression, Survival Analysis, Applied Multivariate Analysis, Probability and Distribution Theory
• 3.8 GPA
• Thesis: The Impact of Race/Ethnicity, Sex, and COVID-19 On the Length of Time from Stroke Onset to Hospital Arrival at Memorial Hermann Hospital

• Relevant Coursework: Intermediate Biostatics, Intro to Data Science, Fundamental of Data Analytics and Prediction

Fundamental of Data Analytics and Prediction

• 4.0 GPA

• Relevant Coursework: Statistics for Science, Probability, Biostatistics, Multivariate Statistics
• 3.6 GPA