GitHub Contribution Activity
Exploring Complex Systems by Building Models, Simulation, and Interactive Tools to Understand how Systems Behave and Feel.
Industrial-grade RAG system for welding defect diagnosis with FAISS-powered semantic search and confidence scoring.
Explore Project →Predicting emotional volatility in music using acoustic features and machine learning analysis.
Read Research →Paddy yield prediction for Sri Lanka using Stacked Ensemble Learning. Published in SciForum IECAG 2025.
View Publication →Satellite telemetry simulator with real-time anomaly detection using Neural Networks and predictive analytics.
Explore Project →Downscaling GRACE satellite data for Sri Lankan water storage predictions using XGBoost and ensemble methods.
Read Research →I'm Deshad Senevirathne, a Manufacturing & Industrial Engineering graduate from the University of Peradeniya, specializing in Machine Learning, Data Science, and Full-Stack Development.
My academic journey at the University of Peradeniya provided me with a robust foundation in engineering principles, which I now combine with advanced skills in Machine Learning, Predictive Analytics, and Full-Stack Development.
My goal is to leverage computational intelligence from building neural networks for anomaly detection to designing predictive models and scalable applications to deliver data-driven solutions that solve real-world problems across diverse industries.
On-site project participation during my tenure as Mechanical Site Engineer
Continuous learning in Space Technology, Robotics, AI/ML, and Engineering