Exploring Space, Code, and Beyond. Leveraging Machine Learning, Predictive Analytics, and Satellite Technology to push the boundaries of aerospace data science.
Satellite telemetry simulator with real-time anomaly detection using Neural Networks and predictive analytics.
Explore Project βDownscaling GRACE satellite data for Sri Lankan water storage predictions using XGBoost and ensemble methods.
Read Research βPaddy yield prediction for Sri Lanka using Stacked Ensemble Learning. Published in SciForum IECAG 2025.
View Publication βRemote resource estimation of Near Earth Asteroids using autoencoders and spectral analysis.
Learn More βI'm Deshad Senevirathne, a Manufacturing & Industrial Engineering graduate from the University of Peradeniya, transitioning my passion and expertise towards the exciting frontier of Satellite Technology and Aerospace Data Science.
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 intelligenceβfrom building neural networks for anomaly detection to designing control algorithms for satellite systemsβto contribute meaningfully to next-generation space missions and Earth observation initiatives.
On-site project participation during my tenure as Mechanical Site Engineer
Continuous learning in Space Technology, Robotics, AI/ML, and Engineering