Rushil Madhu Email & Phone Number

College Park, Md, Us

College Park, Md, Us

• Scraped, lemmatized, processed, and embedded the constitutions, codes, and 10000+ cases from all the United States Courts.• Successfully designed, developed, and implemented 3 relational databases to support 3 operations, ensuring data integrity, security, and scalability.• Collaborated with Machine Learning Engineers to design and implement RESTful APIs to process Single-Line Diagrams for electrical engineers for businesses, bringing in revenue.• Implemented an API to handle .dwg files and engineered prompts to process them.• Streamlined the software delivery process by setting up CI/CD pipelines using GitHub Actions, and containerized the application with Docker, ensuring consistent and efficient deployment across environments.

Contributed to building the iOS app while navigating a tight release timeline

• One of 20 people selected from the city for the Microsoft Industrial Exposure Program• Oversaw the project as team leader and marketing head.• Developed “AutoAid”, an app that enables phones to act as crash detectors in automobiles• Worked with CoreMotion, CallKit, UIKit, and SwiftUI to develop the front-end functionalities.• Utilized the Java Spring-Boot framework and PostgreSQL to create a User Management System.

Developed Python code to analyze TESS satellite data based on certain parameters to provide insights into young stars' behavior. Currently under review at the Journal of Student Research.

• Led a team of interns in developing analytical tools and pipelines for astronomical data processing, ensuring efficient collaboration and task management resulting in a 30% increase in computational efficiency.• Integrated libraries like NumPy for numerical operations, Pandas for data manipulation, and SciPy for signal processing to refine the data, correct noise, and handle missing values.• Explored the application of machine learning algorithms to classify different types of stellar variability observed in the light curves, using models like Random Forest and Support Vector Machines.