I’m Rishan Abebe, an Economics major at NYU Abu Dhabi with minors in Computer Science and Applied Mathematics. I specialize in data science and machine learning, with hands‑on experience in Python, R, SQL, and C++. I excel at transforming complex datasets into clear, actionable insights and collaborating across teams to drive data‑informed decisions.
Rishan Abebe
New York University, Saadiyat Marina District, Abu Dhabi, UAE
About Me
Education
New York University, Abu Dhabi, UAE
Bachelor of Science, Economics (Minor: Computer Science & Applied Mathematics)
Relevant Coursework: Data Analysis, Econometrics, Data Bootcamp, Data Structures, Algorithms, Linear Algebra, Statistics & Probability, Multivariable Calculus
Experience
Research Assistant – Fairness in Machine Learning
New York University, Abu Dhabi, UAE | June 2025 – Present
- Cleaned and prepared the Adult Income dataset in R, improving model input quality and enabling bias analysis across demographic groups.
- Conducted exploratory data analysis and created visualizations using R, identifying bias patterns and improving bias detection efficiency by 20%.
- Performed statistical hypothesis testing to evaluate distributional fairness, supporting evidence‑based recommendations on algorithmic bias.
- Designed and implemented an SVM model predicting recidivism risk with over 75% accuracy, optimizing performance through hyperparameter tuning.
Data Science Intern
WifOR Institute, Berlin, Germany | June 2024 – August 2024
- Built predictive models using scikit‑learn (Logistic Regression, Random Forest) to evaluate health investment outcomes across 10+ countries.
- Restructured and queried macroeconomic datasets using SQL and Python, improving data processing efficiency by 25%.
- Collaborated with data scientists and policy analysts to improve predictive model accuracy by 10%.
- Created stakeholder‑ready visualizations in Tableau to support policy reports and sustainability recommendations.
Data Analyst Intern
25hours Hotel One Central, Dubai, UAE | December 2023 – January 2024
- Analyzed historical booking data and seasonal occupancy trends to identify peak demand periods, improving forecasting accuracy by 10%.
- Created Tableau dashboards to visualize booking trends, guest demographics, and revenue patterns for data‑driven decision‑making.
- Analyzed guest feedback and survey data to identify service improvement opportunities and enhance customer experience.
Projects
Heart Disease Prediction using Machine Learning
Technologies: scikit‑learn, Matplotlib, Seaborn
- Designed and implemented a supervised ML system to predict heart disease risk, achieving over 85% accuracy through hyperparameter tuning.
- Performed end‑to‑end data preprocessing to improve model reliability and generalizability.
- Evaluated and compared classifiers (Logistic Regression, Decision Tree, Random Forest) using precision, recall, F1‑score, and confusion matrices.
Library Management System
Technologies: C++, Custom Data Structures, File I/O
- Developed a C++ library management system with features for adding, editing, and issuing books, managing borrowers, and organizing categories.
- Designed a custom tree structure for category management and a dynamic vector data structure to handle records.
- Implemented file I/O with CSV support to import/export data, ensuring persistence for 10,000+ records.
Life Expectancy Prediction using Regression Models
Technologies: scikit‑learn, Seaborn, Matplotlib
- Analyzed global health and socioeconomic data from the WHO dataset to identify key factors influencing life expectancy across countries.
- Performed feature engineering and multicollinearity reduction to improve model interpretability and predictive accuracy.
- Built and evaluated regression models including Gradient Boosting, achieving an R² score of 0.89 and outperforming baseline models.
- Visualized variable importance, trends, and residuals using Seaborn and Matplotlib to support actionable public health insights.
Multilingual Dictionary
Technologies: C++, Hash Tables, File Processing
- Created a multilingual dictionary using C++ with a custom hash table to support fast word translation lookup between English, Spanish, and French.
- Developed a command‑line interface enabling users to translate across multiple language pairs with real‑time interaction.
- Imported over 50,000 word pairs and applied hashing algorithms, improving lookup speed by over 80%.
Technical Skills
Languages:
Python, R, SQL, C++
ML Libraries:
Scikit-learn, PyTorch, Pandas, NumPy, Matplotlib, GridSearchCV, ggplot2, tidyverse
Frameworks & Tools:
Git, GitHub, Tableau, Excel, Microsoft PowerPoint
Data Skills:
Data Analysis, Data Manipulation, EDA, Causal Inference, Hypothesis Testing, Data Visualization