Shahabeddin Abbaspour Tazehkand

Innovative data scientist with expertise in statistical modeling, machine learning, and educational data mining. Over four years of experience leveraging large and complex datasets to solve challenging problems. Proficient in quantitative analysis, experimentation, and presenting actionable insights. Strong background in Python, R, SQL, and data visualization, with proven success in academic and industry settings.

• Increased student retention by integrating machine learning insights into course planning.
• Awarded multiple conference presentation acceptances for cutting-edge research in analytics and education.
• Maintained a GPA of 4.0 in M.Sc. in Statistics and Data Science program.

• Abbaspour, S., Wang, M., 2024, Leveraging XGBoost to Reduce Failure, Withdrawal, and Dropout Rates, 5th International Conference on Modern Management based on Big Data (MMBD), Beijing, China
• Abbaspour Tazehkand, S., 2024, Enhancing Student Graduation Rates by Mitigating Failure, Dropout, and Withdrawal in Introduction to Statistical Courses Using Statistical and Machine Learning.

Leveraging Machine Learning to Enhance Educational Outcomes, Developed XGBoost models to predict failure, dropout, and withdrawal (FDW) rates in introductory statistics courses., Identified key predictors using SHAP values and data visualization, improving targeted interventions by 30%. 

Dynamic Technology in Teaching Calculus, Designed and implemented GeoGebra-driven tools to teach proof and justification concepts, increasing student comprehension by 25%.