HRUTHVIK HS

Conducted advanced anomaly detection in spacecraft Electrical Power Systems (EPS) using unsupervised machine learning techniques, improving system robustness.

Built predictive models with Vector AutoRegression (VAR) to forecast subsystem failures, enabling proactive preventive maintenance strategies.

Analyzed extensive real telemetry data from the RAAVANA satellite mission, utilizing Python (Pandas, NumPy, Scikit-learn) for comprehensive preprocessing and feature extraction.

Successfully implemented a critical anomaly detection system for spacecraft, directly contributing to enhanced space mission reliability.

Gained specialized expertise in advanced time-series analysis and aerospace applications, applying cutting-edge data science methodologies.

Developed and implemented machine learning models for aircraft type identification, leveraging computer vision algorithms for enhanced accuracy.

Optimized algorithms and model training to significantly enhance image recognition accuracy in aerospace applications.

Applied industry-standard software development practices within the aerospace domain, ensuring robust and scalable solutions.

Successfully deployed a functional aircraft recognition system, demonstrating practical application of advanced ML techniques.

Gained expertise in industry-specific machine learning applications, contributing to specialized aerospace projects.