Harshil Parmar

Highly-motivated employee with desire to take on new challenges. Strong work ethic, adaptability, and exceptional interpersonal skills. Adept at working effectively unsupervised and quickly mastering new skills. Committed job seeker with a history of meeting company needs with consistent and organized practices. Skilled in working under pressure and adapting to new situations and challenges to best enhance the organizational brand. Motivated high school student with a track record of academic excellence looking for part-time work. Intends to develop professional work experience utilizing excellent communication and time management skills.

• CS course Mentorship with Professor Mousumi Chakrabarty at Rutgers University Newark, enhancing skills and gaining valuable hands-on experience in computer science. https://sasn.rutgers.edu/mousumi-chakrabarty
• Dean List Fall 2024

Xcode iOS Development (SwiftUI):

• Birthday Countdown App: Created a countdown and notifications app for birthdays.
• Happiee Quiz Game: Developed an interactive quiz game for kids with a user-friendly design.

Android Development (Android Studio):

• Weather App: Built a dynamic weather app using the OpenWeatherMap API and JSON parsing to display real-time data with a responsive UI.
• Address Book App: Developed a contact manager using Java, SQLite, and AndroidX with full CRUD functionality and a modern UI.
• Cannon Game: Built a 2D physics-based shooting game with touch controls and trajectory mechanics using Java/Kotlin.

Research Experience

Rutgers University – Data Science & Addiction Research Methods 1
Data Science & Adolescent Research | Jan – May 2025

• Substance Use Data Analysis (Lab 1): Applied core Python skills to public health data, including variables, logic, conditionals, string processing, error handling, and data manipulation with pandas to explore substance use patterns.
• ABCD Dataset Exploration (Lab 2): Conducted full-cycle data analysis on adolescent behavior data; performed univariate, bivariate, and categorical analysis, handled missing data and outliers, and visualized trends using seaborn and matplotlib.
• Ensured ethical data handling by adhering to NIMH DUC clauses on privacy, security, and re-identification; documented responsible use of sensitive variables including race, ethnicity, and substance history.
• Compared self-reported and biological (hair toxicology) data to explore concordance patterns and limitations in generalizability.